diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e01e7583..7a255161 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,11 +3,11 @@ ci:
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 24.4.2
+  rev: 24.10.0
   hooks:
     - id: black-jupyter
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.8.5
+  rev: 1.9.1
   hooks:
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
@@ -35,7 +35,7 @@ repos:
             |examples/samplers/MLDA_variance_reduction_linear_regression\.ipynb$
 
 - repo: https://github.com/FlamingTempura/bibtex-tidy
-  rev: v1.11.0
+  rev: v1.14.0
   hooks:
     - id: bibtex-tidy
       files: examples/references.bib
@@ -99,7 +99,7 @@ repos:
       language: pygrep
       types_or: [markdown, rst, jupyter]
 - repo: https://github.com/mwouts/jupytext
-  rev: v1.16.3
+  rev: v1.16.5
   hooks:
   - id: jupytext
     files: ^examples/.+\.ipynb$
diff --git a/examples/generalized_linear_models/GLM-missing-values-in-covariates.ipynb b/examples/generalized_linear_models/GLM-missing-values-in-covariates.ipynb
new file mode 100644
index 00000000..c89ab0f5
--- /dev/null
+++ b/examples/generalized_linear_models/GLM-missing-values-in-covariates.ipynb
@@ -0,0 +1,9814 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(GLM-missing-values-in-covariates.ipynb)=\n",
+    "# GLM-missing-values-in-covariates\n",
+    "\n",
+    ":::{post} Nov 09, 2024\n",
+    ":tags: missing-covariate-values, missing-values, auto-imputation, linear-regression, bayesian-workflow\n",
+    ":category: intermediate, reference\n",
+    ":author: Jonathan Sedar\n",
+    ":::\n",
+    "\n",
+    "Minimal Reproducible Example: Workflow to handle missing data in multiple covariates (numeric predictor features)\n",
+    "\n",
+    "\n",
+    "## Automatic Imputation of Missing Values In Covariates: Worked Example with Bayesian Workflow\n",
+    "\n",
+    "Here we demonstrate **automatic imputation of missing values within multiple covariates (numeric predictor features)**:\n",
+    "\n",
+    "```\n",
+    "y ~ x + e\n",
+    "y: Numeric target\n",
+    "x: Numeric with missing values in covariates (numeric predictor features)\n",
+    "```\n",
+    "\n",
+    "**Disclaimer:**\n",
+    "+ This Notebook is a worked example only, it's not intended to be an academic reference\n",
+    "+ The theory and math may be incorrect, incorrectly notated, or incorrectly used\n",
+    "+ The code may contain errors, inefficiencies, legacy methods, and the text may have typos\n",
+    "+ Use at your own risk!\n",
+    "\n",
+    "## Contents\n",
+    "\n",
+    "+ [Discussion](#Discussion)\n",
+    "\n",
+    "+ [Setup](#Setup)\n",
+    "\n",
+    "+ [0. Curate Dataset](#0.-Curate-Dataset)\n",
+    "\n",
+    "+ [1. `Model0`: Baseline without Missing Values](#1.-`Model0`:-Baseline-without-Missing-Values)\n",
+    "\n",
+    "+ [2. `ModelA`: Auto-impute Missing Values](#2.-`ModelA`:-Auto-impute-Missing-Values)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem Statement\n",
+    "\n",
+    "We often encounter real-world situations and datasets where a predictor feature is numeric and where observations contain\n",
+    "missing values in that feature. \n",
+    "\n",
+    "Missing values break the model inference, because the log-likelihood for those observations can't be computed.\n",
+    "\n",
+    "We have a few options to mitigate the situation:\n",
+    "\n",
+    "+ Firstly, we should always try to learn _why_ the values are missing. If they are _not_ Missing Completely At Random \n",
+    "  (MCAR) {cite:p}`enders2022` and contain latent information about a biased or lossy data-generating process, then we \n",
+    "  might choose to **remove the observations with missing vales** or **ignore the features that contain missing values**\n",
+    "+ If we believe the values **are** Missing Completely At Random (MCAR), then we might choose to \n",
+    "  **auto-impute the missing values** so that we can make use of all the available observations. This is particularly \n",
+    "  acute when we have few observations and/or a high-degree of missingness.\n",
+    "\n",
+    "Here we demonstrate method(s) to auto-impute missing values as part of the `pymc` posterior sampling process. We get\n",
+    "inference and prediction as usual, but also auto-imputed values for the missing values. Pretty neat!\n",
+    "\n",
+    "\n",
+    "## Data & Models Demonstrated\n",
+    "\n",
+    "Our problem statement is that when faced with data with missing values, we want to:\n",
+    "\n",
+    "1. **Infer** the missing values for the in-sample dataset and sample full posterior parameters\n",
+    "2. **Predict** the endogenous feature and the missing values for an out-of-sample dataset\n",
+    "    \n",
+    "    \n",
+    "This notebook takes the opportunity to:\n",
+    "\n",
+    "+ Demonstrate a general method using auto-imputation, which is often mentioned in `pymc` folklore but rarely\n",
+    "  shown in full. A helpful primer and related discussion is this PyMCon2020 talk: {cite:p}`junpenglao2020`\n",
+    "+ Demonstrate a reasonably complete Bayesian workflow {cite:p}`gelman2020bayesian` including data creation\n",
+    "\n",
+    "This notebook is a partner to another `pymc-examples` notebook **Missing_Data_Imputation.ipynb**\n",
+    "which goes into more detail of taxonomies and a much more complicated dataset and tutorial-style worked example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../extra_installs.md\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to install in a Google Colab environment\n",
+    "# !pip install watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# suppress seaborn, it's far too chatty\n",
+    "import warnings  # #noqa\n",
+    "\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm\n",
+    "import pytensor.tensor as pt\n",
+    "\n",
+    "from pymc.testing import assert_no_rvs\n",
+    "\n",
+    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)  # noqa\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "sns.set_theme(\n",
+    "    style=\"darkgrid\",\n",
+    "    palette=\"muted\",\n",
+    "    context=\"notebook\",\n",
+    "    rc={\"figure.dpi\": 72, \"savefig.dpi\": 144, \"figure.figsize\": (12, 4)},\n",
+    ")\n",
+    "\n",
+    "SAMPLE_KWS = dict(\n",
+    "    progressbar=True,\n",
+    "    draws=500,\n",
+    "    tune=3000,  # tune a little more than usual\n",
+    "    chains=4,\n",
+    "    idata_kwargs=dict(log_likelihood=True),\n",
+    ")\n",
+    "\n",
+    "RNG = np.random.default_rng(seed=42)\n",
+    "KWS_MN = dict(markerfacecolor=\"w\", markeredgecolor=\"#333333\", marker=\"d\", markersize=12)\n",
+    "KWS_BOX = dict(kind=\"box\", showmeans=True, whis=(3, 97), meanprops=KWS_MN)\n",
+    "KWS_PNT = dict(estimator=np.mean, errorbar=(\"ci\", 94), join=False, color=\"#32CD32\")\n",
+    "KWS_SCTR = dict(s=80, color=\"#32CD32\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 0. Curate Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**IMPLEMENTATION NOTE**\n",
+    "\n",
+    "+ The auto-imputation and full workflow will be simplest to demonstrate if we\n",
+    "  can compare to true values for the missing data, which means we have to\n",
+    "  synthesize the dataset\n",
+    "+ We will create create at least 2 features that contain missing values, in\n",
+    "  order to demonstrate the flattening effect of `pymc`'s auto-imputation routine\n",
+    "+ We create missing values that are Missing At Random (MAR) {cite:p}`enders2022`\n",
+    "+ We also take the opportunity to make the missingness a real problem, with a\n",
+    "  large proportion of values missing (`40%`)\n",
+    "+ For simplicity, we will assume the original features are each Normally\n",
+    "  distributed: to make this example harder, the reader could experiment with\n",
+    "  other distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0.1 Create Synthetic Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.1.0 Create \"true\" (unobserved) dataset (without missing values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "REFVAL_X_MU = dict(a=1, b=1, c=10, d=2)\n",
+    "REFVAL_X_SIGMA = dict(a=1, b=4, c=1, d=10)\n",
+    "REFVAL_BETA = dict(intercept=-4, a=10, b=-20, c=30, d=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>o35</th>\n",
+       "      <td>2.128972</td>\n",
+       "      <td>3.761941</td>\n",
+       "      <td>9.470507</td>\n",
+       "      <td>19.679299</td>\n",
+       "      <td>323.667882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o36</th>\n",
+       "      <td>0.886053</td>\n",
+       "      <td>-0.709011</td>\n",
+       "      <td>10.232676</td>\n",
+       "      <td>3.302745</td>\n",
+       "      <td>343.178076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o37</th>\n",
+       "      <td>0.159844</td>\n",
+       "      <td>1.634159</td>\n",
+       "      <td>10.021852</td>\n",
+       "      <td>11.827395</td>\n",
+       "      <td>324.313195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o38</th>\n",
+       "      <td>0.175519</td>\n",
+       "      <td>3.502362</td>\n",
+       "      <td>11.601779</td>\n",
+       "      <td>-2.992956</td>\n",
+       "      <td>260.791421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o39</th>\n",
+       "      <td>1.650593</td>\n",
+       "      <td>-0.237386</td>\n",
+       "      <td>9.760644</td>\n",
+       "      <td>-9.849438</td>\n",
+       "      <td>260.662351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            a         b          c          d           y\n",
+       "oid                                                      \n",
+       "o35  2.128972  3.761941   9.470507  19.679299  323.667882\n",
+       "o36  0.886053 -0.709011  10.232676   3.302745  343.178076\n",
+       "o37  0.159844  1.634159  10.021852  11.827395  324.313195\n",
+       "o38  0.175519  3.502362  11.601779  -2.992956  260.791421\n",
+       "o39  1.650593 -0.237386   9.760644  -9.849438  260.662351"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "N = 40\n",
+    "dfraw = pd.DataFrame(\n",
+    "    {\n",
+    "        \"a\": RNG.normal(loc=REFVAL_X_MU[\"a\"], scale=REFVAL_X_SIGMA[\"a\"], size=N),\n",
+    "        \"b\": RNG.normal(loc=REFVAL_X_MU[\"b\"], scale=REFVAL_X_SIGMA[\"b\"], size=N),\n",
+    "        \"c\": RNG.normal(loc=REFVAL_X_MU[\"c\"], scale=REFVAL_X_SIGMA[\"c\"], size=N),\n",
+    "        \"d\": RNG.normal(loc=REFVAL_X_MU[\"d\"], scale=REFVAL_X_SIGMA[\"d\"], size=N),\n",
+    "    },\n",
+    "    index=[f\"o{str(i).zfill(2)}\" for i in range(N)],\n",
+    ")\n",
+    "dfraw.index.name = \"oid\"\n",
+    "dfraw[\"y\"] = (\n",
+    "    REFVAL_BETA[\"intercept\"]\n",
+    "    + (dfraw * np.array(list(REFVAL_BETA.values()))[1:]).sum(axis=1)\n",
+    "    + RNG.normal(loc=0, scale=1, size=N)\n",
+    ")\n",
+    "dfraw.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.1.1 Force 2 features to contain 40% unobserved missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>o00</th>\n",
+       "      <td>1.304717</td>\n",
+       "      <td>3.973017</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>201.150103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o03</th>\n",
+       "      <td>1.940565</td>\n",
+       "      <td>1.928645</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>342.518597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o04</th>\n",
+       "      <td>-0.951035</td>\n",
+       "      <td>1.466743</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.351112</td>\n",
+       "      <td>273.873646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o06</th>\n",
+       "      <td>1.127840</td>\n",
+       "      <td>4.485715</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.633029</td>\n",
+       "      <td>287.578749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o08</th>\n",
+       "      <td>0.983199</td>\n",
+       "      <td>3.715654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>223.797089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o09</th>\n",
+       "      <td>0.146956</td>\n",
+       "      <td>1.270316</td>\n",
+       "      <td>10.446531</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>249.089611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o10</th>\n",
+       "      <td>1.879398</td>\n",
+       "      <td>2.156478</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>281.480703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o14</th>\n",
+       "      <td>1.467509</td>\n",
+       "      <td>-0.881491</td>\n",
+       "      <td>8.312666</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>212.866620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o15</th>\n",
+       "      <td>0.140708</td>\n",
+       "      <td>-1.555511</td>\n",
+       "      <td>8.552888</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>244.497563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o17</th>\n",
+       "      <td>0.041117</td>\n",
+       "      <td>6.979765</td>\n",
+       "      <td>9.002753</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>178.964784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o18</th>\n",
+       "      <td>1.878450</td>\n",
+       "      <td>-2.463324</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>485.762125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o20</th>\n",
+       "      <td>0.815138</td>\n",
+       "      <td>-5.731479</td>\n",
+       "      <td>9.621837</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>439.462555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o21</th>\n",
+       "      <td>0.319070</td>\n",
+       "      <td>-0.339540</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-7.895381</td>\n",
+       "      <td>305.700832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o23</th>\n",
+       "      <td>0.845471</td>\n",
+       "      <td>3.344889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>284.174248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o25</th>\n",
+       "      <td>0.647866</td>\n",
+       "      <td>4.173389</td>\n",
+       "      <td>9.794562</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>203.615652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o26</th>\n",
+       "      <td>1.532309</td>\n",
+       "      <td>-0.394900</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-4.120968</td>\n",
+       "      <td>270.298830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o29</th>\n",
+       "      <td>1.430821</td>\n",
+       "      <td>0.234783</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.570486</td>\n",
+       "      <td>273.296300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o30</th>\n",
+       "      <td>3.141648</td>\n",
+       "      <td>-4.102745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.413652</td>\n",
+       "      <td>426.296015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o31</th>\n",
+       "      <td>0.593585</td>\n",
+       "      <td>-3.533149</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>337.768688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o33</th>\n",
+       "      <td>0.186227</td>\n",
+       "      <td>2.988643</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-2.863079</td>\n",
+       "      <td>181.853726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o34</th>\n",
+       "      <td>1.615979</td>\n",
+       "      <td>1.569703</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>289.133447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o35</th>\n",
+       "      <td>2.128972</td>\n",
+       "      <td>3.761941</td>\n",
+       "      <td>9.470507</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>323.667882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o36</th>\n",
+       "      <td>0.886053</td>\n",
+       "      <td>-0.709011</td>\n",
+       "      <td>10.232676</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>343.178076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o39</th>\n",
+       "      <td>1.650593</td>\n",
+       "      <td>-0.237386</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-9.849438</td>\n",
+       "      <td>260.662351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            a         b          c          d           y\n",
+       "oid                                                      \n",
+       "o00  1.304717  3.973017        NaN        NaN  201.150103\n",
+       "o03  1.940565  1.928645        NaN        NaN  342.518597\n",
+       "o04 -0.951035  1.466743        NaN  10.351112  273.873646\n",
+       "o06  1.127840  4.485715        NaN  16.633029  287.578749\n",
+       "o08  0.983199  3.715654        NaN        NaN  223.797089\n",
+       "o09  0.146956  1.270316  10.446531        NaN  249.089611\n",
+       "o10  1.879398  2.156478        NaN        NaN  281.480703\n",
+       "o14  1.467509 -0.881491   8.312666        NaN  212.866620\n",
+       "o15  0.140708 -1.555511   8.552888        NaN  244.497563\n",
+       "o17  0.041117  6.979765   9.002753        NaN  178.964784\n",
+       "o18  1.878450 -2.463324        NaN        NaN  485.762125\n",
+       "o20  0.815138 -5.731479   9.621837        NaN  439.462555\n",
+       "o21  0.319070 -0.339540        NaN  -7.895381  305.700832\n",
+       "o23  0.845471  3.344889        NaN        NaN  284.174248\n",
+       "o25  0.647866  4.173389   9.794562        NaN  203.615652\n",
+       "o26  1.532309 -0.394900        NaN  -4.120968  270.298830\n",
+       "o29  1.430821  0.234783        NaN   3.570486  273.296300\n",
+       "o30  3.141648 -4.102745        NaN   0.413652  426.296015\n",
+       "o31  0.593585 -3.533149        NaN        NaN  337.768688\n",
+       "o33  0.186227  2.988643        NaN  -2.863079  181.853726\n",
+       "o34  1.615979  1.569703        NaN        NaN  289.133447\n",
+       "o35  2.128972  3.761941   9.470507        NaN  323.667882\n",
+       "o36  0.886053 -0.709011  10.232676        NaN  343.178076\n",
+       "o39  1.650593 -0.237386        NaN  -9.849438  260.662351"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1.038265</td>\n",
+       "      <td>0.825850</td>\n",
+       "      <td>-0.951035</td>\n",
+       "      <td>0.445586</td>\n",
+       "      <td>1.024615</td>\n",
+       "      <td>1.624633</td>\n",
+       "      <td>3.141648</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1.052709</td>\n",
+       "      <td>2.918529</td>\n",
+       "      <td>-5.731479</td>\n",
+       "      <td>-0.744110</td>\n",
+       "      <td>1.601931</td>\n",
+       "      <td>3.508059</td>\n",
+       "      <td>6.979765</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>9.680004</td>\n",
+       "      <td>0.737447</td>\n",
+       "      <td>8.312666</td>\n",
+       "      <td>9.277186</td>\n",
+       "      <td>9.640341</td>\n",
+       "      <td>10.014834</td>\n",
+       "      <td>11.601779</td>\n",
+       "      <td>16</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1.018293</td>\n",
+       "      <td>11.149556</td>\n",
+       "      <td>-19.320463</td>\n",
+       "      <td>-6.570904</td>\n",
+       "      <td>0.502871</td>\n",
+       "      <td>6.264410</td>\n",
+       "      <td>31.138625</td>\n",
+       "      <td>16</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>285.251112</td>\n",
+       "      <td>75.686022</td>\n",
+       "      <td>166.591767</td>\n",
+       "      <td>228.845718</td>\n",
+       "      <td>282.827475</td>\n",
+       "      <td>323.829210</td>\n",
+       "      <td>485.762125</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count        mean        std         min         25%         50%  \\\n",
+       "a   40.0    1.038265   0.825850   -0.951035    0.445586    1.024615   \n",
+       "b   40.0    1.052709   2.918529   -5.731479   -0.744110    1.601931   \n",
+       "c   24.0    9.680004   0.737447    8.312666    9.277186    9.640341   \n",
+       "d   24.0    1.018293  11.149556  -19.320463   -6.570904    0.502871   \n",
+       "y   40.0  285.251112  75.686022  166.591767  228.845718  282.827475   \n",
+       "\n",
+       "          75%         max   0        1  \n",
+       "a    1.624633    3.141648   0  float64  \n",
+       "b    3.508059    6.979765   0  float64  \n",
+       "c   10.014834   11.601779  16  float64  \n",
+       "d    6.264410   31.138625  16  float64  \n",
+       "y  323.829210  485.762125   0  float64  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = dfraw.copy()\n",
+    "\n",
+    "prop_missing = 0.4\n",
+    "df.loc[RNG.choice(df.index, int(N * prop_missing), replace=False), \"c\"] = np.nan\n",
+    "df.loc[RNG.choice(df.index, int(N * prop_missing), replace=False), \"d\"] = np.nan\n",
+    "idx = df[[\"c\", \"d\"]].isnull().sum(axis=1) > 0\n",
+    "display(df.loc[idx])\n",
+    "display(pd.concat((df.describe(include=\"all\").T, df.isnull().sum(), df.dtypes), axis=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Features `a` and `b` are full, complete, without missing values\n",
+    "+ Features `c` and `d` contain missing values, where observations can even contain missing values for both features\n",
+    "\n",
+    "> NOTE we dont need any further steps to prepare the dataset (e.g. clean observations & features, force datatypes, set \n",
+    "> indexes, etc), so we will move straight to EDA and transformations for model input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0.2 Very limited quick EDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.2.1 Univariate: target `y`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x230.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 228,
+       "width": 851
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_univariate_violin(df: pd.DataFrame, fts: list):\n",
+    "    v_kws = dict(data=df, cut=0)\n",
+    "    cs = sns.color_palette(n_colors=len(fts))\n",
+    "    height_bump = 2 if len(fts) == 1 else 1\n",
+    "    f, axs = plt.subplots(\n",
+    "        len(fts), 1, figsize=(12, 0.8 + height_bump * 1.2 * len(fts)), squeeze=False, sharex=True\n",
+    "    )\n",
+    "    for i, ft in enumerate(fts):\n",
+    "        ax = sns.violinplot(x=ft, **v_kws, ax=axs[i][0], color=cs[i])\n",
+    "        n_nans = pd.isnull(df[ft]).sum()\n",
+    "        _ = ax.text(\n",
+    "            0.993,\n",
+    "            0.93,\n",
+    "            f\"NaNs: {n_nans}\",\n",
+    "            transform=ax.transAxes,\n",
+    "            ha=\"right\",\n",
+    "            va=\"top\",\n",
+    "            backgroundcolor=\"w\",\n",
+    "            fontsize=10,\n",
+    "        )\n",
+    "        _ = ax.set_title(f\"ft: {ft}\")\n",
+    "    _ = f.suptitle(\"Univariate numerics with NaN count noted\")\n",
+    "    _ = f.tight_layout()\n",
+    "\n",
+    "\n",
+    "plot_univariate_violin(df, fts=[\"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ `y` Looks smooth, reasonably central, can probably model with a Normal likelihood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.2.2 Univariate: predictors `a, b, c, d`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x403.2 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 398,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_univariate_violin(df, fts=[\"a\", \"b\", \"c\", \"d\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ `a`, `b`, `c`, `d` Vary across the range, but all reasonably central, can probably model with Normal distributions\n",
+    "+ `c`, `d` each contain `16` NaN values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.2.3 Bivariate: target vs predictors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 285,
+       "width": 853
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfp = df.reset_index().melt(id_vars=[\"oid\", \"y\"], var_name=\"ft\")\n",
+    "g = sns.lmplot(\n",
+    "    y=\"y\",\n",
+    "    x=\"value\",\n",
+    "    hue=\"ft\",\n",
+    "    col=\"ft\",\n",
+    "    data=dfp,\n",
+    "    fit_reg=True,\n",
+    "    height=4,\n",
+    "    aspect=0.75,\n",
+    "    facet_kws={\"sharex\": False},\n",
+    ")\n",
+    "_ = g.fig.suptitle(\"Bivariate plots of `y` vs fts `a`, `b`, `c`, `d`\")\n",
+    "_ = g.fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Each of features `a`, `b`, `c`, `d` has a correlation to `y`, some stronger, some weaker\n",
+    "+ This looks fairly realistic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0.3 Transform dataset to `dfx` for model input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**IMPORTANT NOTE**\n",
+    "\n",
+    "+ Reminder that Bayesian inferential methods **do not need** a `test` dataset (nor k-fold cross validation)\n",
+    "  to fit parameters. \n",
+    "+ We also **do not need** a `holdout` (out-of-sample) dataset (that contains target `y` values) to evaluate model\n",
+    "  performance, because we can use in-sample PPC, LOO-PIT and ELPD evaluations\n",
+    "+ Depending on the real-world model implementation we might:\n",
+    "  + Create a separate `holdout` set (even though we dont need it) to simply eyeball the behaviour of predictive outputs\n",
+    "  + Create a `forecast` set (which does not have target `y` values) to demonstrate how we would use the model and it's\n",
+    "    predictive outputs on future new data in Production\n",
+    "+ Here we **do** take a `holdout` set, in order to eyeball the predictive outputs, and also to eyeball the auto-imputed\n",
+    "  missing values compared to the true synthetic data. This is only possible because we synthesized the true data above.\n",
+    "+ Per the following terminology, `df` created above is our \"working\" dataset, which we will partition into \"train\" and \n",
+    "  \"holdout\"\n",
+    "\n",
+    "**Dataset terminology / partitioning / purpose:**\n",
+    "\n",
+    "```text\n",
+    "|<---------- Relevant domain of all data for our analyses & models ---------->|\n",
+    "|<----- \"Observed\" historical target ------>||<- \"Unobserved\" future target ->|\n",
+    "|<----------- \"Working\" dataset ----------->||<----- \"Forecast\" dataset ----->|\n",
+    "|<- Training/CrossVal ->||<- Test/Holdout ->|\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.3.1 Separate `df_train` and `df_holdout`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE**\n",
+    "\n",
+    "+ We have full control over how many observations we create in `df`, so the ratio of this split doesn't really matter,\n",
+    "  and we'll arrange to have `10` observations in the holdout set\n",
+    "+ Eyeball the `count` of non-nulls in the below tabels to ensure we have missing values in both features `c`, `d` in \n",
+    "  both datasets `train` and `holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(30, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.986483</td>\n",
+       "      <td>0.869626</td>\n",
+       "      <td>-0.951035</td>\n",
+       "      <td>0.219438</td>\n",
+       "      <td>0.934626</td>\n",
+       "      <td>1.578862</td>\n",
+       "      <td>3.141648</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.868533</td>\n",
+       "      <td>2.838965</td>\n",
+       "      <td>-4.828623</td>\n",
+       "      <td>-0.873470</td>\n",
+       "      <td>1.518223</td>\n",
+       "      <td>3.126624</td>\n",
+       "      <td>6.979765</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.818891</td>\n",
+       "      <td>8.312666</td>\n",
+       "      <td>9.134305</td>\n",
+       "      <td>9.640341</td>\n",
+       "      <td>10.019513</td>\n",
+       "      <td>11.601779</td>\n",
+       "      <td>12</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.549915</td>\n",
+       "      <td>9.740175</td>\n",
+       "      <td>-19.320463</td>\n",
+       "      <td>-5.345297</td>\n",
+       "      <td>0.502871</td>\n",
+       "      <td>7.547417</td>\n",
+       "      <td>16.633029</td>\n",
+       "      <td>12</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>287.402426</td>\n",
+       "      <td>76.524934</td>\n",
+       "      <td>166.591767</td>\n",
+       "      <td>234.020837</td>\n",
+       "      <td>285.876498</td>\n",
+       "      <td>322.786791</td>\n",
+       "      <td>485.762125</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count        mean        std         min         25%         50%  \\\n",
+       "a   30.0    0.986483   0.869626   -0.951035    0.219438    0.934626   \n",
+       "b   30.0    0.868533   2.838965   -4.828623   -0.873470    1.518223   \n",
+       "c   18.0    9.663899   0.818891    8.312666    9.134305    9.640341   \n",
+       "d   18.0    0.549915   9.740175  -19.320463   -5.345297    0.502871   \n",
+       "y   30.0  287.402426  76.524934  166.591767  234.020837  285.876498   \n",
+       "\n",
+       "          75%         max   0        1  \n",
+       "a    1.578862    3.141648   0  float64  \n",
+       "b    3.126624    6.979765   0  float64  \n",
+       "c   10.019513   11.601779  12  float64  \n",
+       "d    7.547417   16.633029  12  float64  \n",
+       "y  322.786791  485.762125   0  float64  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train = df.sample(n=len(df) - 10, replace=False).sort_index()\n",
+    "print(df_train.shape)\n",
+    "display(\n",
+    "    pd.concat(\n",
+    "        (df_train.describe(include=\"all\").T, df_train.isnull().sum(), df_train.dtypes), axis=1\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.193610</td>\n",
+       "      <td>0.694911</td>\n",
+       "      <td>-0.302180</td>\n",
+       "      <td>0.848872</td>\n",
+       "      <td>1.367769</td>\n",
+       "      <td>1.621022</td>\n",
+       "      <td>2.128972</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.605238</td>\n",
+       "      <td>3.238522</td>\n",
+       "      <td>-5.731479</td>\n",
+       "      <td>-0.119344</td>\n",
+       "      <td>2.699954</td>\n",
+       "      <td>3.920248</td>\n",
+       "      <td>4.873113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.728319</td>\n",
+       "      <td>0.466786</td>\n",
+       "      <td>9.094521</td>\n",
+       "      <td>9.508340</td>\n",
+       "      <td>9.708200</td>\n",
+       "      <td>9.874776</td>\n",
+       "      <td>10.486972</td>\n",
+       "      <td>4</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.423426</td>\n",
+       "      <td>15.688134</td>\n",
+       "      <td>-11.766861</td>\n",
+       "      <td>-8.417320</td>\n",
+       "      <td>-0.275241</td>\n",
+       "      <td>5.069154</td>\n",
+       "      <td>31.138625</td>\n",
+       "      <td>4</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>278.797170</td>\n",
+       "      <td>76.757413</td>\n",
+       "      <td>181.932062</td>\n",
+       "      <td>217.877327</td>\n",
+       "      <td>271.797565</td>\n",
+       "      <td>317.009141</td>\n",
+       "      <td>439.462555</td>\n",
+       "      <td>0</td>\n",
+       "      <td>float64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count        mean        std         min         25%         50%  \\\n",
+       "a   10.0    1.193610   0.694911   -0.302180    0.848872    1.367769   \n",
+       "b   10.0    1.605238   3.238522   -5.731479   -0.119344    2.699954   \n",
+       "c    6.0    9.728319   0.466786    9.094521    9.508340    9.708200   \n",
+       "d    6.0    2.423426  15.688134  -11.766861   -8.417320   -0.275241   \n",
+       "y   10.0  278.797170  76.757413  181.932062  217.877327  271.797565   \n",
+       "\n",
+       "          75%         max  0        1  \n",
+       "a    1.621022    2.128972  0  float64  \n",
+       "b    3.920248    4.873113  0  float64  \n",
+       "c    9.874776   10.486972  4  float64  \n",
+       "d    5.069154   31.138625  4  float64  \n",
+       "y  317.009141  439.462555  0  float64  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_holdout = df.loc[list(set(df.index.values) - set(df_train.index.values))].copy().sort_index()\n",
+    "print(df_holdout.shape)\n",
+    "display(\n",
+    "    pd.concat(\n",
+    "        (df_holdout.describe(include=\"all\").T, df_holdout.isnull().sum(), df_holdout.dtypes), axis=1\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.3.2 Create `dfx_train`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform (zscore and scale) numerics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.874024e+02</td>\n",
+       "      <td>76.524934</td>\n",
+       "      <td>166.591767</td>\n",
+       "      <td>234.020837</td>\n",
+       "      <td>285.876498</td>\n",
+       "      <td>322.786791</td>\n",
+       "      <td>485.762125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intercept</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-1.073216e-16</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-2.266079</td>\n",
+       "      <td>-0.897119</td>\n",
+       "      <td>-0.060651</td>\n",
+       "      <td>0.692833</td>\n",
+       "      <td>2.520633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-9.251859e-17</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-2.041078</td>\n",
+       "      <td>-0.624095</td>\n",
+       "      <td>0.232760</td>\n",
+       "      <td>0.808990</td>\n",
+       "      <td>2.189426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>7.401487e-16</td>\n",
+       "      <td>1.028992</td>\n",
+       "      <td>-1.697915</td>\n",
+       "      <td>-0.665470</td>\n",
+       "      <td>-0.029602</td>\n",
+       "      <td>0.446852</td>\n",
+       "      <td>2.435075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>4.317534e-17</td>\n",
+       "      <td>1.028992</td>\n",
+       "      <td>-2.099187</td>\n",
+       "      <td>-0.622794</td>\n",
+       "      <td>-0.004970</td>\n",
+       "      <td>0.739244</td>\n",
+       "      <td>1.699085</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count          mean        std         min         25%         50%  \\\n",
+       "y           30.0  2.874024e+02  76.524934  166.591767  234.020837  285.876498   \n",
+       "intercept   30.0  1.000000e+00   0.000000    1.000000    1.000000    1.000000   \n",
+       "a           30.0 -1.073216e-16   1.017095   -2.266079   -0.897119   -0.060651   \n",
+       "b           30.0 -9.251859e-17   1.017095   -2.041078   -0.624095    0.232760   \n",
+       "c           18.0  7.401487e-16   1.028992   -1.697915   -0.665470   -0.029602   \n",
+       "d           18.0  4.317534e-17   1.028992   -2.099187   -0.622794   -0.004970   \n",
+       "\n",
+       "                  75%         max  \n",
+       "y          322.786791  485.762125  \n",
+       "intercept    1.000000    1.000000  \n",
+       "a            0.692833    2.520633  \n",
+       "b            0.808990    2.189426  \n",
+       "c            0.446852    2.435075  \n",
+       "d            0.739244    1.699085  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "FTS_NUM = [\"a\", \"b\", \"c\", \"d\"]\n",
+    "FTS_NON_NUM = []\n",
+    "FTS_Y = [\"y\"]\n",
+    "MNS = np.nanmean(df_train[FTS_NUM], axis=0)\n",
+    "SDEVS = np.nanstd(df_train[FTS_NUM], axis=0)\n",
+    "\n",
+    "dfx_train_num = (df_train[FTS_NUM] - MNS) / SDEVS\n",
+    "icpt = pd.Series(np.ones(len(df_train)), name=\"intercept\", index=dfx_train_num.index)\n",
+    "\n",
+    "# concat including y which will be used as observed\n",
+    "dfx_train = pd.concat((df_train[FTS_Y], icpt, df_train[FTS_NON_NUM], dfx_train_num), axis=1)\n",
+    "display(dfx_train.describe().T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.3.3 Create `dfx_holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>278.797170</td>\n",
+       "      <td>76.757413</td>\n",
+       "      <td>181.932062</td>\n",
+       "      <td>217.877327</td>\n",
+       "      <td>271.797565</td>\n",
+       "      <td>317.009141</td>\n",
+       "      <td>439.462555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intercept</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.242251</td>\n",
+       "      <td>0.812752</td>\n",
+       "      <td>-1.507192</td>\n",
+       "      <td>-0.160947</td>\n",
+       "      <td>0.445944</td>\n",
+       "      <td>0.742143</td>\n",
+       "      <td>1.336230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.263934</td>\n",
+       "      <td>1.160242</td>\n",
+       "      <td>-2.364538</td>\n",
+       "      <td>-0.353919</td>\n",
+       "      <td>0.656130</td>\n",
+       "      <td>1.093316</td>\n",
+       "      <td>1.434692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.080948</td>\n",
+       "      <td>0.586548</td>\n",
+       "      <td>-0.715462</td>\n",
+       "      <td>-0.195471</td>\n",
+       "      <td>0.055667</td>\n",
+       "      <td>0.264981</td>\n",
+       "      <td>1.034246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.197925</td>\n",
+       "      <td>1.657358</td>\n",
+       "      <td>-1.301194</td>\n",
+       "      <td>-0.947335</td>\n",
+       "      <td>-0.087173</td>\n",
+       "      <td>0.477431</td>\n",
+       "      <td>3.231515</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count        mean        std         min         25%         50%  \\\n",
+       "y           10.0  278.797170  76.757413  181.932062  217.877327  271.797565   \n",
+       "intercept   10.0    1.000000   0.000000    1.000000    1.000000    1.000000   \n",
+       "a           10.0    0.242251   0.812752   -1.507192   -0.160947    0.445944   \n",
+       "b           10.0    0.263934   1.160242   -2.364538   -0.353919    0.656130   \n",
+       "c            6.0    0.080948   0.586548   -0.715462   -0.195471    0.055667   \n",
+       "d            6.0    0.197925   1.657358   -1.301194   -0.947335   -0.087173   \n",
+       "\n",
+       "                  75%         max  \n",
+       "y          317.009141  439.462555  \n",
+       "intercept    1.000000    1.000000  \n",
+       "a            0.742143    1.336230  \n",
+       "b            1.093316    1.434692  \n",
+       "c            0.264981    1.034246  \n",
+       "d            0.477431    3.231515  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfx_holdout_num = (df_holdout[FTS_NUM] - MNS) / SDEVS\n",
+    "icpt = pd.Series(np.ones(len(df_holdout)), name=\"intercept\", index=dfx_holdout_num.index)\n",
+    "\n",
+    "# concat including y which will be used as observed\n",
+    "dfx_holdout = pd.concat((df_holdout[FTS_Y], icpt, df_holdout[FTS_NON_NUM], dfx_holdout_num), axis=1)\n",
+    "display(dfx_holdout.describe().T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. `Model0`: Baseline without Missing Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section might seem unusual or unnecessary, but will hopefully provide a useful comparison for general behaviour\n",
+    "and help to further explain the model architecture used in `ModelA`. \n",
+    "\n",
+    "We will create `Model0` using the same general linear model, operating on the `dfrawx_train` dataset _without_ any\n",
+    "missing values:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\sigma_{\\beta} &\\sim \\text{InverseGamma}(11, 10)  \\\\\n",
+    "\\beta_{j} &\\sim \\text{Normal}(0, \\sigma_{\\beta}, \\text{shape}=j)  \\\\\n",
+    "\\\\\n",
+    "\\epsilon &\\sim \\text{InverseGamma}(11, 10)  \\\\\n",
+    "\\hat{y_{i}} &\\sim \\text{Normal}(\\mu=\\beta_{j}^{T}\\mathbb{x}_{ij}, \\sigma=\\epsilon)  \\\\\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where:\n",
+    "+ Observations $i$ (`observation_id` aka `oid`) contain numeric features $j$ that have complete values (this is all features `a`, `b`, `c`, `d`)\n",
+    "+ Our target is $\\hat{y_{i}}$, here of `y` with linear sub-model $\\beta_{j}^{T}\\mathbb{x}_{ij}$ \n",
+    "  to regress onto those features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.0 Quickly prepare non-missing datasets based on `dfraw`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a lightly simplifed copy of the same logic / workflow in $\\S0.3$ above. We won't take up any more space here \n",
+    "with EDA, the only difference is `c` and `d` are now complete"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Partition `dfraw` into `dfraw_train` and `dfraw_holdout`, use same indexes as `df_train` and `df_holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>o25</th>\n",
+       "      <td>0.647866</td>\n",
+       "      <td>4.173389</td>\n",
+       "      <td>9.794562</td>\n",
+       "      <td>-2.153573</td>\n",
+       "      <td>203.615652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o26</th>\n",
+       "      <td>1.532309</td>\n",
+       "      <td>-0.394900</td>\n",
+       "      <td>9.049978</td>\n",
+       "      <td>-4.120968</td>\n",
+       "      <td>270.298830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o29</th>\n",
+       "      <td>1.430821</td>\n",
+       "      <td>0.234783</td>\n",
+       "      <td>8.272680</td>\n",
+       "      <td>3.570486</td>\n",
+       "      <td>273.296300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o35</th>\n",
+       "      <td>2.128972</td>\n",
+       "      <td>3.761941</td>\n",
+       "      <td>9.470507</td>\n",
+       "      <td>19.679299</td>\n",
+       "      <td>323.667882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>o39</th>\n",
+       "      <td>1.650593</td>\n",
+       "      <td>-0.237386</td>\n",
+       "      <td>9.760644</td>\n",
+       "      <td>-9.849438</td>\n",
+       "      <td>260.662351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            a         b         c          d           y\n",
+       "oid                                                     \n",
+       "o25  0.647866  4.173389  9.794562  -2.153573  203.615652\n",
+       "o26  1.532309 -0.394900  9.049978  -4.120968  270.298830\n",
+       "o29  1.430821  0.234783  8.272680   3.570486  273.296300\n",
+       "o35  2.128972  3.761941  9.470507  19.679299  323.667882\n",
+       "o39  1.650593 -0.237386  9.760644  -9.849438  260.662351"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfraw_train = dfraw.loc[df_train.index].copy()\n",
+    "dfraw_holdout = dfraw.loc[df_holdout.index].copy()\n",
+    "dfraw_holdout.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `dfrawx_train`: Transform (zscore and scale) numerics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.874024e+02</td>\n",
+       "      <td>76.524934</td>\n",
+       "      <td>166.591767</td>\n",
+       "      <td>234.020837</td>\n",
+       "      <td>285.876498</td>\n",
+       "      <td>322.786791</td>\n",
+       "      <td>485.762125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intercept</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-1.073216e-16</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-2.266079</td>\n",
+       "      <td>-0.897119</td>\n",
+       "      <td>-0.060651</td>\n",
+       "      <td>0.692833</td>\n",
+       "      <td>2.520633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-9.251859e-17</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-2.041078</td>\n",
+       "      <td>-0.624095</td>\n",
+       "      <td>0.232760</td>\n",
+       "      <td>0.808990</td>\n",
+       "      <td>2.189426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-1.021405e-15</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-1.866273</td>\n",
+       "      <td>-0.580940</td>\n",
+       "      <td>-0.043790</td>\n",
+       "      <td>0.739403</td>\n",
+       "      <td>2.189560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-3.700743e-17</td>\n",
+       "      <td>1.017095</td>\n",
+       "      <td>-2.069592</td>\n",
+       "      <td>-0.801472</td>\n",
+       "      <td>-0.032248</td>\n",
+       "      <td>0.691756</td>\n",
+       "      <td>2.173761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count          mean        std         min         25%         50%  \\\n",
+       "y           30.0  2.874024e+02  76.524934  166.591767  234.020837  285.876498   \n",
+       "intercept   30.0  1.000000e+00   0.000000    1.000000    1.000000    1.000000   \n",
+       "a           30.0 -1.073216e-16   1.017095   -2.266079   -0.897119   -0.060651   \n",
+       "b           30.0 -9.251859e-17   1.017095   -2.041078   -0.624095    0.232760   \n",
+       "c           30.0 -1.021405e-15   1.017095   -1.866273   -0.580940   -0.043790   \n",
+       "d           30.0 -3.700743e-17   1.017095   -2.069592   -0.801472   -0.032248   \n",
+       "\n",
+       "                  75%         max  \n",
+       "y          322.786791  485.762125  \n",
+       "intercept    1.000000    1.000000  \n",
+       "a            0.692833    2.520633  \n",
+       "b            0.808990    2.189426  \n",
+       "c            0.739403    2.189560  \n",
+       "d            0.691756    2.173761  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MNS_RAW = np.nanmean(dfraw_train[FTS_NUM], axis=0)\n",
+    "SDEVS_RAW = np.nanstd(dfraw_train[FTS_NUM], axis=0)\n",
+    "\n",
+    "dfrawx_train_num = (dfraw_train[FTS_NUM] - MNS_RAW) / SDEVS_RAW\n",
+    "icpt = pd.Series(np.ones(len(dfraw_train)), name=\"intercept\", index=dfrawx_train_num.index)\n",
+    "\n",
+    "# concat including y which will be used as observed\n",
+    "dfrawx_train = pd.concat(\n",
+    "    (dfraw_train[FTS_Y], icpt, dfraw_train[FTS_NON_NUM], dfrawx_train_num), axis=1\n",
+    ")\n",
+    "display(dfrawx_train.describe().T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `dfrawx_holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>278.797170</td>\n",
+       "      <td>76.757413</td>\n",
+       "      <td>181.932062</td>\n",
+       "      <td>217.877327</td>\n",
+       "      <td>271.797565</td>\n",
+       "      <td>317.009141</td>\n",
+       "      <td>439.462555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intercept</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.242251</td>\n",
+       "      <td>0.812752</td>\n",
+       "      <td>-1.507192</td>\n",
+       "      <td>-0.160947</td>\n",
+       "      <td>0.445944</td>\n",
+       "      <td>0.742143</td>\n",
+       "      <td>1.336230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.263934</td>\n",
+       "      <td>1.160242</td>\n",
+       "      <td>-2.364538</td>\n",
+       "      <td>-0.353919</td>\n",
+       "      <td>0.656130</td>\n",
+       "      <td>1.093316</td>\n",
+       "      <td>1.434692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>-0.289949</td>\n",
+       "      <td>0.823264</td>\n",
+       "      <td>-1.915581</td>\n",
+       "      <td>-0.786253</td>\n",
+       "      <td>-0.166341</td>\n",
+       "      <td>0.059979</td>\n",
+       "      <td>0.814883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.224142</td>\n",
+       "      <td>1.401435</td>\n",
+       "      <td>-1.293270</td>\n",
+       "      <td>-0.824576</td>\n",
+       "      <td>-0.011119</td>\n",
+       "      <td>0.532746</td>\n",
+       "      <td>3.116343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count        mean        std         min         25%         50%  \\\n",
+       "y           10.0  278.797170  76.757413  181.932062  217.877327  271.797565   \n",
+       "intercept   10.0    1.000000   0.000000    1.000000    1.000000    1.000000   \n",
+       "a           10.0    0.242251   0.812752   -1.507192   -0.160947    0.445944   \n",
+       "b           10.0    0.263934   1.160242   -2.364538   -0.353919    0.656130   \n",
+       "c           10.0   -0.289949   0.823264   -1.915581   -0.786253   -0.166341   \n",
+       "d           10.0    0.224142   1.401435   -1.293270   -0.824576   -0.011119   \n",
+       "\n",
+       "                  75%         max  \n",
+       "y          317.009141  439.462555  \n",
+       "intercept    1.000000    1.000000  \n",
+       "a            0.742143    1.336230  \n",
+       "b            1.093316    1.434692  \n",
+       "c            0.059979    0.814883  \n",
+       "d            0.532746    3.116343  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfrawx_holdout_num = (dfraw_holdout[FTS_NUM] - MNS_RAW) / SDEVS_RAW\n",
+    "icpt = pd.Series(np.ones(len(dfraw_holdout)), name=\"intercept\", index=dfrawx_holdout_num.index)\n",
+    "\n",
+    "# concat including y which will be used as observed\n",
+    "dfrawx_holdout = pd.concat(\n",
+    "    (dfraw_holdout[FTS_Y], icpt, dfraw_holdout[FTS_NON_NUM], dfrawx_holdout_num), axis=1\n",
+    ")\n",
+    "display(dfrawx_holdout.describe().T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the inevitable (but slight) difference in `MNS` vs `MNS_RAW` and `SDEVS` vs `SDEVS_RAW`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.98648324 0.86853301 9.66389924 0.54991532]\n",
+      "[0.98648324 0.86853301 9.82613624 0.81663988]\n",
+      "[0.85500914 2.7912481  0.79581928 9.46574849]\n",
+      "[0.85500914 2.7912481  0.81095867 9.72998886]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(MNS)\n",
+    "print(MNS_RAW)\n",
+    "print(SDEVS)\n",
+    "print(SDEVS_RAW)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Build Model Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ft_y = \"y\"\n",
+    "FTS_XJ = [\"intercept\", \"a\", \"b\", \"c\", \"d\"]\n",
+    "\n",
+    "COORDS = dict(\n",
+    "    xj_nm=FTS_XJ,  # these are the names of the features\n",
+    "    oid=dfrawx_train.index.values,  # these are the observation_ids\n",
+    ")\n",
+    "\n",
+    "with pm.Model(coords=COORDS) as mdl0:\n",
+    "    # 0. create (Mutable)Data containers for obs (Y, X)\n",
+    "    y = pm.Data(\"y\", dfrawx_train[ft_y].values, dims=\"oid\")\n",
+    "    xj = pm.Data(\"xj\", dfrawx_train[FTS_XJ].values, dims=(\"oid\", \"xj_nm\"))\n",
+    "\n",
+    "    # 2. define priors for contiguous data\n",
+    "    b_s = pm.Gamma(\"beta_sigma\", alpha=10, beta=10)  # E ~ 1\n",
+    "    bj = pm.Normal(\"beta_j\", mu=0, sigma=b_s, dims=\"xj_nm\")\n",
+    "\n",
+    "    # 4. define evidence\n",
+    "    epsilon = pm.Gamma(\"epsilon\", alpha=50, beta=50)  # encourage E ~ 1\n",
+    "    lm = pt.dot(xj, bj.T)\n",
+    "    _ = pm.Normal(\"yhat\", mu=lm, sigma=epsilon, observed=y, dims=\"oid\")\n",
+    "\n",
+    "RVS_PPC = [\"yhat\"]\n",
+    "RVS_PRIOR = [\"epsilon\", \"beta_sigma\", \"beta_j\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Verify the built model structure matches our intent, and validate the parameterization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>clusteroid (30)</title>\n",
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+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusteroid (30) x xj_nm (5)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-243.23C20,-243.23 124,-243.23 124,-243.23 130,-243.23 136,-249.23 136,-255.23 136,-255.23 136,-329.23 136,-329.23 136,-335.23 130,-341.23 124,-341.23 124,-341.23 20,-341.23 20,-341.23 14,-341.23 8,-335.23 8,-329.23 8,-329.23 8,-255.23 8,-255.23 8,-249.23 14,-243.23 20,-243.23\"/>\n",
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+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clusterxj_nm (5)</title>\n",
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+       "</g>\n",
+       "<!-- y -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>y</title>\n",
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+       "<text text-anchor=\"middle\" x=\"188\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- yhat -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>yhat</title>\n",
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+       "<text text-anchor=\"middle\" x=\"188\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- yhat&#45;&gt;y -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>yhat&#45;&gt;y</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"191.5,-109.79 188,-99.79 184.5,-109.79 191.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- xj -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>xj</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M115,-333.23C115,-333.23 85,-333.23 85,-333.23 79,-333.23 73,-327.23 73,-321.23 73,-321.23 73,-287.73 73,-287.73 73,-281.73 79,-275.73 85,-275.73 85,-275.73 115,-275.73 115,-275.73 121,-275.73 127,-281.73 127,-287.73 127,-287.73 127,-321.23 127,-321.23 127,-327.23 121,-333.23 115,-333.23\"/>\n",
+       "<text text-anchor=\"middle\" x=\"100\" y=\"-315.93\" font-family=\"Times,serif\" font-size=\"14.00\">xj</text>\n",
+       "<text text-anchor=\"middle\" x=\"100\" y=\"-299.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"100\" y=\"-282.93\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- xj&#45;&gt;yhat -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>xj&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M111.73,-275.35C118.05,-261.55 126.5,-245 136,-231.32 140.56,-224.75 145.88,-218.17 151.33,-211.96\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"153.63,-214.63 157.77,-204.88 148.45,-209.92 153.63,-214.63\"/>\n",
+       "</g>\n",
+       "<!-- beta_sigma -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>beta_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"290\" cy=\"-421.79\" rx=\"55.33\" ry=\"40.66\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"290\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"290\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- beta_j -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>beta_j</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"290\" cy=\"-304.48\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+       "</g>\n",
+       "<!-- beta_sigma&#45;&gt;beta_j -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>beta_sigma&#45;&gt;beta_j</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"293.5,-356.93 290,-346.93 286.5,-356.93 293.5,-356.93\"/>\n",
+       "</g>\n",
+       "<!-- epsilon -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>epsilon</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"188\" cy=\"-304.48\" rx=\"42.6\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"188\" y=\"-315.93\" font-family=\"Times,serif\" font-size=\"14.00\">epsilon</text>\n",
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+       "<text text-anchor=\"middle\" x=\"188\" y=\"-282.93\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- epsilon&#45;&gt;yhat -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>epsilon&#45;&gt;yhat</title>\n",
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+       "</g>\n",
+       "<!-- beta_j&#45;&gt;yhat -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>beta_j&#45;&gt;yhat</title>\n",
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+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x140232b90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'unobserved': [beta_sigma ~ Gamma(10, f()),\n",
+       "  beta_j ~ Normal(0, beta_sigma),\n",
+       "  epsilon ~ Gamma(50, f())],\n",
+       " 'observed': [yhat ~ Normal(f(beta_j), epsilon)]}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "point={'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0., 0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n",
+      "point={'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0., 0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(pm.model_to_graphviz(mdl0, formatting=\"plain\"))\n",
+    "display(dict(unobserved=mdl0.unobserved_RVs, observed=mdl0.observed_RVs))\n",
+    "assert_no_rvs(mdl0.logp())\n",
+    "mdl0.debug(fn=\"logp\", verbose=True)\n",
+    "mdl0.debug(fn=\"random\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ This is a very straightforward model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Sample Prior Predictive, View Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [beta_j, beta_sigma, epsilon, yhat]\n"
+     ]
+    }
+   ],
+   "source": [
+    "GRP = \"prior\"\n",
+    "kws = dict(samples=2000, return_inferencedata=True, random_seed=42)\n",
+    "with mdl0:\n",
+    "    id0 = pm.sample_prior_predictive(var_names=RVS_PPC + RVS_PRIOR, **kws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.1 In-Sample Prior PPC (Retrodictive Check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 237,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_ppc_retrodictive(\n",
+    "    idata: az.InferenceData,\n",
+    "    grp: str = \"posterior\",\n",
+    "    rvs: list = None,\n",
+    "    mdlnm: str = \"mdla\",\n",
+    "    ynm: str = \"y\",\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience plot prior or posterior PPC retrodictive KDE\"\"\"\n",
+    "    f, axs = plt.subplots(1, 1, figsize=(12, 3))\n",
+    "    _ = az.plot_ppc(idata, group=grp, kind=\"kde\", var_names=rvs, ax=axs, observed=True)\n",
+    "    _ = f.suptitle(f\"In-sample {grp.title()} PPC Retrodictive KDE on `{ynm}` - `{mdlnm}`\")\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_ppc_retrodictive(id0, grp=GRP, rvs=[\"yhat\"], mdlnm=\"mdl0\", ynm=\"y\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ The prior PPC is wrong as expected, because we've set relatively uninformative priors\n",
+    "+ However the general range and scale is reasonable and the sampler should be able to \n",
+    "  find the highest likelihood latent space easily"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2.2 Quick look at selected priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Coefficients etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x345.6 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 340,
+       "width": 851
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_krushke(\n",
+    "    idata: az.InferenceData,\n",
+    "    group: str = \"posterior\",\n",
+    "    rvs: list = RVS_PRIOR,\n",
+    "    coords: dict = None,\n",
+    "    ref_vals: list = None,\n",
+    "    mdlnm: str = \"mdla\",\n",
+    "    n: int = 1,\n",
+    "    nrows: int = 1,\n",
+    ") -> plt.figure:\n",
+    "    \"\"\"Convenience plot Krushke-style posterior (or prior) KDE\"\"\"\n",
+    "    m = int(np.ceil(n / nrows))\n",
+    "    f, axs = plt.subplots(nrows, m, figsize=(3 * m, 0.8 + nrows * 2))\n",
+    "    _ = az.plot_posterior(\n",
+    "        idata, group=group, ax=axs, var_names=rvs, coords=coords, ref_val=ref_vals\n",
+    "    )\n",
+    "    _ = f.suptitle(f\"{group.title()} distributions for rvs {rvs} - `{mdlnm}\")\n",
+    "    _ = f.tight_layout()\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_krushke(id0, GRP, rvs=RVS_PRIOR, mdlnm=\"mdl0\", n=1 + 1 + 5, nrows=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Model priors `beta_sigma`, `beta_j: (levels)`, `epsilon` all have reasonable prior ranges as specified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Sample Posterior, View Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3.1 Sample Posterior and PPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_sigma, beta_j, epsilon]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cbbd8ca271747a4ad3d4ce9184be2b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 3_000 tune and 500 draw iterations (12_000 + 2_000 draws total) took 7 seconds.\n",
+      "Sampling: [yhat]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f7910c92679414d806dd089f746771e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GRP = \"posterior\"\n",
+    "with mdl0:\n",
+    "    id0.extend(pm.sample(**SAMPLE_KWS), join=\"right\")\n",
+    "    id0.extend(\n",
+    "        pm.sample_posterior_predictive(trace=id0.posterior, var_names=RVS_PPC),\n",
+    "        join=\"right\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3.2 View Traces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>epsilon</th>\n",
+       "      <td>1.004</td>\n",
+       "      <td>0.101</td>\n",
+       "      <td>0.808</td>\n",
+       "      <td>1.183</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>1694.0</td>\n",
+       "      <td>1363.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_sigma</th>\n",
+       "      <td>20.902</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>19.305</td>\n",
+       "      <td>22.399</td>\n",
+       "      <td>0.018</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>2064.0</td>\n",
+       "      <td>1567.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[intercept]</th>\n",
+       "      <td>287.374</td>\n",
+       "      <td>0.185</td>\n",
+       "      <td>287.008</td>\n",
+       "      <td>287.709</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2442.0</td>\n",
+       "      <td>1763.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[a]</th>\n",
+       "      <td>8.456</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>8.062</td>\n",
+       "      <td>8.817</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1470.0</td>\n",
+       "      <td>1604.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[b]</th>\n",
+       "      <td>-55.912</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>-56.271</td>\n",
+       "      <td>-55.539</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1967.0</td>\n",
+       "      <td>1566.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[c]</th>\n",
+       "      <td>24.226</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>23.886</td>\n",
+       "      <td>24.569</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>1508.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[d]</th>\n",
+       "      <td>48.753</td>\n",
+       "      <td>0.188</td>\n",
+       "      <td>48.412</td>\n",
+       "      <td>49.096</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2369.0</td>\n",
+       "      <td>1163.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      mean     sd   hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "epsilon              1.004  0.101    0.808    1.183      0.002    0.002   \n",
+       "beta_sigma          20.902  0.832   19.305   22.399      0.018    0.013   \n",
+       "beta_j[intercept]  287.374  0.185  287.008  287.709      0.004    0.003   \n",
+       "beta_j[a]            8.456  0.202    8.062    8.817      0.005    0.004   \n",
+       "beta_j[b]          -55.912  0.202  -56.271  -55.539      0.005    0.003   \n",
+       "beta_j[c]           24.226  0.186   23.886   24.569      0.004    0.003   \n",
+       "beta_j[d]           48.753  0.188   48.412   49.096      0.004    0.003   \n",
+       "\n",
+       "                   ess_bulk  ess_tail  r_hat  \n",
+       "epsilon              1694.0    1363.0    1.0  \n",
+       "beta_sigma           2064.0    1567.0    1.0  \n",
+       "beta_j[intercept]    2442.0    1763.0    1.0  \n",
+       "beta_j[a]            1470.0    1604.0    1.0  \n",
+       "beta_j[b]            1967.0    1566.0    1.0  \n",
+       "beta_j[c]            2100.0    1508.0    1.0  \n",
+       "beta_j[d]            2369.0    1163.0    1.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x295.2 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 291,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x115.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 116,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_traces_and_display_summary(\n",
+    "    idata: az.InferenceData, rvs: list = None, coords: dict = None, mdlnm=\"mdla\", energy=False\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience to plot traces and display summary table for rvs\"\"\"\n",
+    "    _ = az.plot_trace(idata, var_names=rvs, coords=coords, figsize=(12, 0.8 + 1.1 * len(rvs)))\n",
+    "    f = plt.gcf()\n",
+    "    _ = f.suptitle(f\"Posterior traces of {rvs} - `{mdlnm}`\")\n",
+    "    _ = f.tight_layout()\n",
+    "    if energy:\n",
+    "        _ = az.plot_energy(idata, fill_alpha=(0.8, 0.6), fill_color=(\"C0\", \"C8\"), figsize=(12, 1.6))\n",
+    "    display(az.summary(idata, var_names=rvs))\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_traces_and_display_summary(id0, rvs=RVS_PRIOR, mdlnm=\"mdl0\", energy=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Samples well-mixed and well-behaved: `ess_bulk` is good, `r_hat` is good\n",
+    "+ Marginal energy | energy transition looks a little mismatched, but `E-BFMI >> 0.3` so is\n",
+    "  [apparently reasonable](https://python.arviz.org/en/stable/api/generated/arviz.bfmi.html#arviz.bfmi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3.3 In-Sample Posterior PPC (Retrodictive Check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 237,
+       "width": 685
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_ppc_retrodictive(id0, grp=GRP, rvs=[\"yhat\"], mdlnm=\"mdl0\", ynm=\"y\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ In-sample PPC `yhat` tracks the observed `y` very closely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3.4 In-Sample PPC LOO-PIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x172.8 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 172,
+       "width": 843
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_loo_pit(\n",
+    "    idata: az.InferenceData, mdlname: str = \"mdla\", y: str = \"yhat\", y_hat: str = \"yhat\"\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience plot LOO-PIT KDE and ECDF\"\"\"\n",
+    "    f, axs = plt.subplots(1, 2, figsize=(12, 2.4))\n",
+    "    _ = az.plot_loo_pit(idata, y=y, y_hat=y_hat, ax=axs[0])\n",
+    "    _ = az.plot_loo_pit(idata, y=y, y_hat=y_hat, ax=axs[1], ecdf=True)\n",
+    "    _ = axs[0].set_title(f\"Predicted `{y_hat}` LOO-PIT\")\n",
+    "    _ = axs[1].set_title(f\"Predicted `{y_hat}` LOO-PIT cumulative\")\n",
+    "    _ = f.suptitle(f\"In-sample LOO-PIT `{mdlname}`\")\n",
+    "    _ = f.tight_layout()\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_loo_pit(id0, \"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ `LOO-PIT` looks good, very slightly overdispersed but more than acceptable for use"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.4 Evaluate Posterior Parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4.1 Coefficients etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x345.6 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 340,
+       "width": 845
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_krushke(id0, GRP, RVS_PRIOR, mdlnm=\"mdl0\", n=1 + 1 + 5, nrows=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Posteriors for model coeffs `beta_sigma`, `beta_j: (levels)`, `epsilon` all smooth and central as specified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For interest's sake forestplot the `beta_j` levels to compare relative effects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x151.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 149,
+       "width": 851
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_forest(\n",
+    "    idata: az.InferenceData, grp: str = \"posterior\", rvs: list = None, mdlnm=\"mdla\"\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience forestplot posterior (or prior) KDE\"\"\"\n",
+    "\n",
+    "    n = sum([idata[grp][rv].shape[-1] for rv in rvs])\n",
+    "    f, axs = plt.subplots(1, 1, figsize=(12, 0.6 + 0.3 * n))\n",
+    "    _ = az.plot_forest(idata[grp], var_names=rvs, ax=axs, combined=True)\n",
+    "    _ = f.suptitle(f\"Forestplot of {grp.title()} level values for `{rvs}` - `{mdlnm}`\")\n",
+    "    _ = f.tight_layout()\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_forest(id0, grp=GRP, rvs=[\"beta_j\"], mdlnm=\"mdl0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Very tight and distinct posterior distributions\n",
+    "+ The levels broadly correspond to what we would expect to see, given the synthetic data creation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.5 Create PPC Forecast on `dfrawx_holdout` set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.5.1 Replace dataset with `dfrawx_holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COORDS_F = deepcopy(COORDS)\n",
+    "COORDS_F[\"oid\"] = dfrawx_holdout.index.values\n",
+    "mdl0.set_data(\"xj\", dfrawx_holdout[FTS_XJ].values, coords=COORDS_F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.5.2 Sample PPC for `yhat`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [yhat]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df9fcbb6b17949c1a67820e093ca343d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with mdl0:\n",
+    "    id0_h = pm.sample_posterior_predictive(trace=id0.posterior, var_names=RVS_PPC, predictions=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.5.3 Out-of-sample: Compare forecasted `yhat` to known true value `y`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Extract `yhat` from PPC idata, and attach real values (only available because it's a `holdout` set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>chain</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1.118062</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>draw</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>144.340887</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>124.750000</td>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>374.250000</td>\n",
+       "      <td>499.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yhat</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>278.561418</td>\n",
+       "      <td>72.901021</td>\n",
+       "      <td>177.512164</td>\n",
+       "      <td>202.607237</td>\n",
+       "      <td>271.000294</td>\n",
+       "      <td>324.595923</td>\n",
+       "      <td>442.744943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>278.797170</td>\n",
+       "      <td>72.820296</td>\n",
+       "      <td>181.932062</td>\n",
+       "      <td>203.615652</td>\n",
+       "      <td>271.797565</td>\n",
+       "      <td>323.667882</td>\n",
+       "      <td>439.462555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         count        mean         std         min         25%         50%  \\\n",
+       "chain  20000.0    1.500000    1.118062    0.000000    0.750000    1.500000   \n",
+       "draw   20000.0  249.500000  144.340887    0.000000  124.750000  249.500000   \n",
+       "yhat   20000.0  278.561418   72.901021  177.512164  202.607237  271.000294   \n",
+       "y      20000.0  278.797170   72.820296  181.932062  203.615652  271.797565   \n",
+       "\n",
+       "              75%         max  \n",
+       "chain    2.250000    3.000000  \n",
+       "draw   374.250000  499.000000  \n",
+       "yhat   324.595923  442.744943  \n",
+       "y      323.667882  439.462555  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfraw_h_y = (\n",
+    "    az.extract(id0_h, group=\"predictions\", var_names=[\"yhat\"])\n",
+    "    .to_dataframe()\n",
+    "    .drop([\"chain\", \"draw\"], axis=1)\n",
+    "    .reset_index()\n",
+    "    .set_index([\"oid\"])\n",
+    ")\n",
+    "dfraw_h_y = pd.merge(dfraw_h_y, dfraw_holdout[[\"y\"]], how=\"left\", left_index=True, right_index=True)\n",
+    "dfraw_h_y.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot posterior `yhat` vs known true values `y` (only available because it's a `holdout` set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Fx8fD1dUVJ0+exNOnTxEXF4eiRYuifv366NevH1q1aqU3DcbUvfQxZd0nO/mI+vMtLS1x69YtODs7Y9++fYiMjESJEiXQv39/jB8/PsvnExsbi/379+PUqVN4/Pgx4uPjxe+9R48eWfreBUGAm5sb9u3bh0ePHkEmk+GLL75A165dMWDAAK3ypSZjv5+cun5r1641yz2V2XOqfPnyWr+/ffv2oV69eunSZWg9TU2dT//5559wcnLCjh07xDnMlUolKlasiE6dOmHEiBEZ1ldMxdDnPIAsXZ9r165hx44duHfvHkJCQlCqVCl07NgR3333XU6fEoDU+2Xz5s0AgP79+2d4fz179gzbt2/H1atX8fbtWxQuXBgODg4YPXo06tevjzp16iA5OTldnVSd/06fPh2Ojo5YtGgR7ty5g4IFC6JKlSpYuXIlypUrJ26fnXqcJlPUAwVBwN9//w1fX1+8e/cOBQoUEN+39OzZ0+C8Wk2hUMDb2xv79+/HkydPEB8fj8qVK6NPnz5ZDh4YWs/Maj1apVLBy8sLnp6euHv3rlgeL1euHJo1a4YhQ4ZofU9p9evXD5s2bYKfnx/u3Lmj9942N833cgcPHkS5cuWwdetWnDp1Cu/evUPhwoXx5Zdf4ttvvxWH1Xv//j02bdoEX19ffPjwAZ999hkaN26MyZMn44svvtD5OYIg4N9//8WePXsQGBiIyMhIlC9fHt26dcO3336brbT/+++/AIAvv/xS73dRsWJF2NvbIzAwED4+PjqDVZR7MViVS4WHh8PPzw9A6gubsmXLZnlfuVyOIUOG4JdffkFKSgqOHj2a4YsTc1EoFJg/f366rtpJSUkIDAxEYGAgdu/ejQULFqBnz545koZLly5h4sSJ6ebgev/+Pd6/f4+TJ0/C1dUVmzZtSlewvnbtGiZPnoyIiIh0x9Xcv2PHjlizZo3eAs/Ro0cxZ84crXk63r9/D3d3d5w9exZ79+7FpUuXsHjxYrEXCZD6cPnnn3/g5+eHAwcO6O1ZcvbsWfzwww9ITk4Wlz19+hRPnz7FgQMHsHLlSnTo0CHzi6Xh/PnzmDZtGqKjo7WWv3nzBm/evIGHhwdGjhyJWbNmZbsAk5mEhASMHDkSV69eFZclJibi8uXLuHz5Mvbv349NmzbBxsYm3b7q4TTTpv/du3d49+4dvLy80LlzZyxZsgSFChUCkNpzcdmyZejRowfi4uLw+++/o0WLFuLEjOr91ZWncuXK6ZzQEQBcXV2xYMECqFQqcVlQUBCCgoLg6uqKjRs3ZvhyW5c9e/Zg6dKl6cbpfvXqFV69egU3NzcMHz4cs2fPNvglpOaLKrWUlBQ8f/4cz58/x6FDhzB69GjMmjXLoOMCwOPHj/HDDz/gyZMnWsvDw8Nx4cIFXLhwATt27MCGDRuyHVjNilWrVmHjxo1aywICAhAQEIC9e/fi77//RuXKlU2a/sDAQIwaNSpdt/SQkBCEhITg3Llz2LVrF7Zv325wAFOpVGLx4sXYtWuX1vKkpCTcv38f9+/fx+7du7F69Wo0a9ZM73E0x4IGgODg4CxNNGyq/D27n58V9erVQ9WqVfHkyRMcPXo0w2CVh4cHAKBVq1Y6v4tly5alGw/7/fv38PLywokTJ7BgwQL069cv3X67du3CkiVLoFAotJanpKQgLi4Oz58/h5ubG2bPno2RI0dm4yyzr3379vD09ERsbCxu3bqFhg0baq0PCgoSJyaOiYlBQEBAusrY3bt3xaE60zZ2MUZcXBxGjhwJf39/reUvXrzAzp07ceTIEWzfvh21atUy+NjZeT7UqVMH9vb2CAoKyrTMdeTIEQBA06ZNtQJV4eHh+OGHH7SeaQAQGRkpPtd2794NZ2dnvZUyQRDwww8/wNfXV1z2/PnzdMGF5ORkfPvtt7h8+bK4LDExEaGhobhy5Qrc3NywZs0ag3qsxsbG4qeffsLZs2e1lqekpODu3bu4e/cudu7ciZUrV6Jly5ZZPm5WJSQkYNasWThx4oTW8qioKNy8eRM3b97Erl274OzsjBo1aqTb//379xg3bhwCAwO1lqv3PX/+vNYz21DGlhnj4uIwd+7cdMOuJiUl4fbt27h9+zb279+Pbdu2ZfgCxdSSk5Mxd+5cuLu7p1sXGRmJyMhIBAQEYM+ePdi8eXO6fCSnFChQAF26dIGrqyuuXLmC4OBgnY0jgP/yd1tbW7Rt29Ykn1+iRAm0atUKZ8+ehbe3N/73v//pbbSizhMqVaqk9VLdVPWMnBAQEICJEyfi/fv3Wss/fPiAEydO4MSJE+jatSuWLFmSbn4aY+peWZXduo+x+YjaggULsG/fPvHvN2/eoESJEllO/7Vr1zBt2jQEBwdrLQ8JCcGZM2dw5swZNG7cGH/99ZdWPUSTSqXCtGnT4OXlpbVcfR579+7Ftm3b0gWRjfl+cvr65eV7yhT1tIiICPTp0wdBQUFay9Xlag8PD+zevdug35ohcuo5LwgCli9fnq4c/erVK2zduhVeXl5o3LixSc4hI+fPn8eLFy8AAF27dtW7na53N4mJifDx8cGpU6eyVB9+9uwZNm/ejJiYGACpz/IPHz6I5UJj63Gmqgfevn0bP//8M6KiorSOceXKFVy5cgUnT57E+vXrDX7fEx0dje+//16rHApATNupU6cynHssJ98jKhQKTJ48Od3vPCoqClFRUbh//z527NiBP//8Ex07dtR5jHLlyqFu3bq4efMm9uzZk2uDVZoePXqECRMmiHU7IPV6+vn54eLFi1i+fDnKli2LCRMmaP0ewsLC4O3tDV9fX+zduzdd3p6cnIw5c+bA09NTa/mTJ0/w119/4fjx4wa96wZS8xx1Pli7du0Mt61duzYCAwMREBAAlUqVY+8myfT4TeVS/v7+4oTIulqzZcbJyUks6Fy8eNEkaSpXrhxu3LghtjgBUl8k3rhxA1u2bDH4eHPmzBEfMI0aNcLWrVtx6dIl/Pvvv/j9999RqlQpJCYmYtasWTh+/Li437Fjx3Djxg2xV0vZsmVx48YNg9ORkJCAadOmIT4+HuXLl8eqVatw6tQpXLp0CQcOHEDnzp0BpLa4SDuGaWhoKCZMmICIiAjY2tpi3rx5OH78OC5fvoxjx47ht99+Ewv/Pj4+WunXlJSUhDlz5qB48eL4448/cO7cOXh5eYmTeUZFRWHixIlYtGgRvvrqK7i4uODy5cs4evSo+HB88uQJ9u7dq/c8p02bBqlUiunTp+PMmTM4f/48Fi1aBFtbWyQmJuKHH34waMLFW7duYcKECYiOjkb58uWxZMkS/Pvvv+KLNHXlz8XFBX/++We6/S0sLFC5cmVUrlzZqJbv169fx9WrV9GyZUvs2bMHly9fhpubm1i4vH79us4uwefPn8eUKVMQHR2NokWLYu7cueL3vnPnTjg6OgIAvL29MXXqVK2JyStUqCAOqxkREYGFCxeK61QqFWbOnIno6GjI5XKsXLlSZ6AsNDQUv/32Gz777DMsWLAA58+fx9mzZzFz5kwUKlQIkZGRGD16dLoKakYOHDiABQsWICkpCeXKlcOyZcvg6+uLS5cuYfPmzWLrtR07dmDx4sXift27d8eNGze0Wqyp7yX1sjt37ojfY5MmTeDi4oJz587Bz88PW7ZsEV8Gb9u2DRcuXMhymoHUiuGoUaPw5MkTyOVyfPvttzhy5Aj8/f3h4eGBESNGQCqVIigoCCNGjBArlw0bNhTTqfbdd9+JywxtAX/v3j1s3LgRZcqUwZ9//omLFy/Cx8cH48ePh0wmw+vXrzFy5EidFffspF9t+vTpCAsLg62tLRYtWgQfHx9cvnwZHh4eGDJkCIDUgMCSJUvEfX777TfcuHFDrHw7ODiI5635ElCzgtKhQwfs2rULly9fxr///oulS5eiXLlyiIyMxPjx4/HgwQO912bHjh1o1KgRDh8+jPPnz+OPP/5A//79M72m2c3fTfX5WaWuwDx79gx37tzRuY26hTaQ2ssnreTkZGzbtg3VqlXD6tWrce7cORw/fhzTp09HgQIFoFQqsWjRInz48EFrv/Pnz2PhwoVQKBSoXr061q1bh9OnT+PSpUs4fPgwxo8fj4IFC0IQBKxYsUIrT7hx44bWS+sFCxakW2YsR0dHsRePuvGMprRli7QVTuC/Vm8lSpTQ29sW+O+eXrBggbhM/azX1dvj5s2b8Pf3R5s2bbBz505cvHgR7u7uYuvVqKgocQxyQxjzfFD3zHj48CEePXqk8/h3797F06dPAWj/lpKTkzF27FhcvXpVzEs8PDxw5coV8bOsrKwQGBiI0aNH652P9Pnz5/D19cXAgQNx+vRpnDp1CvPmzUtXjlTnBw4ODti5cycuX76MgwcPij1K/Pz8MHPmzCxfN6VSiQkTJogV+w4dOmD37t24dOkSTp06hTlz5qBIkSJinqOZd2/ZsgU3btwQK6rffPONmKeVK1cu0/VA6ouuadOmiS9I+/btiwMHDsDf3x+nTp3C3LlzUbRoUbx9+xajRo3Cu3fvtNKvUCgwduxYBAYGQiaTYdy4cTh+/DguXbqErVu3olatWjhy5Ei6/bLKFGXGOXPmiPd3+/btxevr5eWFcePGQSqV4vnz55g8eTJUKlWWnu+msGbNGjFQ5eTkhF27duHcuXM4d+4cdu/ejW7dugFIDbap5wH5WNT5gUqlwtGjR3Vuo1AoxOvatWtXvT1NskOdJ0REROjMQ4HU34Y6L9V8oWaqekZOePXqFUaOHIn3799r5ZP+/v44cOCA2DDj2LFj6Xr3GVP3MkR26j7G5iNqycnJ2LdvHzp27AgvLy/4+vpiwYIF6NKlS5bS/uDBA4wbNw7BwcGwsrLC1KlT4e3tLV7fHj16iNdo7Nixel/ovnjxAl5eXqhdu7Y4esexY8cwdOhQAKn1x/Hjx2u9cDfm+/kY189c91RWnkMZyW49La2VK1fi8ePHGD58OI4cOYLLly/D1dUVTZs2BZD6na9evTrT9GSHMc/5zGzZskUMVDVs2FAsl3h6emLYsGF4//692KggJ6mfE/b29nq/18uXL2PGjBlISUkR75GLFy/izJkzmD17NgoWLIglS5ZoBcp1cXNzgyAI4v6urq6YO3eu+CLd2HqcqeqBM2bMgCAI+Pnnn3Hq1ClcvHgR69evF6/PmTNn9D5fMzJt2jSx3tC/f3/x97x79260aNECly5dynAI3uzWM7NSj3ZxcRF/5wMHDsThw4fF3/mSJUtQrFgxpKSkYM6cORnOZanuXezj45Pp7yE3+PXXXxEVFYXp06fj5MmT+PfffzFv3jxYWFhApVJh8eLFmDhxIgoWLIjly5fDz88PZ8+exaRJkyCRSBAfH6/z3d+iRYvEQJWTk5P4XDh06BC6deuGwMDAdIHBzAQHB4vPLl29/TWp8+2kpCStQBzlAQLlSmvXrhXs7e0Fe3t74erVq9k6RsuWLQV7e3uhVatWWsvbtm0r2NvbC6NHj9a7r6enp/j5ly9f1lp3+fJlveuy6sKFC+Ixvv/+e0GhUKTbJjg4WGjdurVgb28vNG7cWIiJidFaP2vWLMHe3l5o27ZtttJw9uxZMQ1XrlxJt16lUgmDBw8W7O3thTZt2mitW7VqlbjvpUuXdB4/ICBA3Gb27Nla6w4dOiSuq1evnvD27dt0n925c2dxm549ewpJSUla2yQlJQktWrQQ7O3thREjRug9fs2aNYWLFy+mS19QUJBQt25dwd7eXhg0aJDe/W/evKkzXR07dhQiIiJ0nvv8+fMFe3t7oXr16sKjR490bpNd6t+v+rejVCrTbTN37lxxm/Pnz4vLk5KShHbt2om/qZcvX6bbV6VSCb/88ou4v4eHR7ptvv/+e3H9qVOnBEEQhE2bNonLNm7cmG6fNWvWaH3nQUFB6ba5ePGiUKNGDcHe3l746aef9O7/4cMHcXl4eLjg4OAgfidhYWHpjpucnCyMHTtW3P/69et6j53W0qVLBXt7e6Fu3bpCQkJCuvURERFC/fr1BXt7e2HmzJnp1mdk+vTp4u/k5MmTOrfx8PAQ0/a///0v3Xr1ujVr1hj02YLwXx6iziffv3+fbhvNe2HVqlUmS39QUJC4/PDhwzr3VR+/du3a6a790KFDBXt7e2Ho0KHp9rt586Z47KVLl+o8dlhYmJi/9uvXT2udZh7fsGHDdHmvmvr61a5dW2u5sfl7Vj/fFIKDg4WaNWsK9vb2wsKFC3Vuo74HGjVqpJUPa/5+vvnmGyE2Njbdvtu3bxe32b9/v9a6QYMGieev675Nu3/a38mrV6/EdYcOHTLwzLNm5MiRgr29vdC3b99068aMGSOmX1+ZolevXoK9vb0wd+5ccZnm9+vp6am1veb99urVq3TH08z/f/75Z51pHj9+vLjNixcvsnyuxj4fwsLChFq1agn29vbCihUrdH7GwoULBXt7e6FBgwZa97T6+VG9enXhzJkzOve9ceOG+Fv9448/tNZp5uHDhw/Xe46a12/06NFCcnJyum1mz54tbpO27KCv/Lh//35xn8WLF+v87MePHwsNGjQQ7O3thU6dOqV7dquPPWvWrAzTrmu9Zpl1165dOvd/8eKF+KyaMmWK1rodO3aI+7u6uqbbNyEhQejTp4+4jb406mNsmdHX11dc9+uvv+rcf+PGjeI2ms+jjJ7vgvDfM1TzHk1Ls3yjeV/GxsYKX331lWBvby8MGzZMZ3lMELTvyTdv3mity0qdxBhdunQR82hdNOsCt2/fNulnJycnC02bNhXs7e2FqVOn6txGncfXqFFDqy5gynqGZjleEIyvB44ePVqwt7cXmjZtqjOfFgRB2Lx5s7i/r6+vuNyYuldmjK37GJuPaH5+x44ddZZ9skJdNqhdu7be3+SGDRvEz9qwYYPWOnX50N7eXhgwYICQmJiYbn/N/EIzzzPm+/kY18+c95QgZPwc0nfPmaKepl6u79omJSWJaXNwcBBUKpXO8zOGsc95fdfnw4cP4j05dOhQneWSrVu3al2DnKBSqcTyrL7noVKpFJ8pbdq0EUJDQ9Ntc/PmTbEsqKt8rlkO01f/M7YeZ8p6YK1atYT79++n2//x48dC9erVBXt7e+G7777T+Rn6nD59Wjz+ypUr061XKBTChAkTxG3S1nVN8R4xo3p07969xXKNLpqff/DgQb3nefHiRXE7Pz8/vduZk2ZdMm35UW3evHni+nr16umsH6nfjdWuXVtISUkRlz948EB8tzVt2jSdedNvv/0mHj/te13N9G3atElcfufOHZ3PMF3++ecfcdvAwMBMrwnlHuxZlUtFRkaK//7ss8+ydQx1F3BdXd3NTd0TqGDBgliwYIHO7u4lS5YUW+RFRkZmq9VGRjRbOKiHKdIkkUgwf/58uLi4aA1DBQBVq1bFoEGDMGDAALE1U1pffvml+N1l9B307t07XQ8jiUSi1Uti1KhR6Vp7Wlpait1e0w7DoalPnz46u3dXq1YNI0aMAJDaC+n169d6j6F28eJFcbizqVOn6h2O66effkKhQoUgCAJcXV0zPW52WFhYYP78+Tq78s6cOVMcmuLQoUPicvUY4QDw448/6hxWTiKR4JdffhGH1UjbfR5I7cWgvr9+++03XL9+HWvWrAEANGvWDGPHjs0w7d999x2qVauWbnmzZs3Elpc+Pj5ITEzM8DgA4OnpKQ4h8PPPP+scDsTCwgKLFi0Se0kYMk+YutWKUqnUypfUbG1t4ezsjL179xrUEj86OlocmuTrr7/WOxRl9+7dxWF53N3d9fYoMNaMGTN0DhHUu3dvcUhG9bAigPHpzyz/AVJ/o1u3bsWRI0cMau2t/n5tbW3F+dPSKlasmDjs3e3bt/X2rnR0dDR4/HtT5u/Z+XxDlCxZUpx/yMvLS2uoVSC1Rb66NVhGre5/+OEHnUOmff311+K/1UOLAKktkFu1aoXevXtj8uTJeofx0Xy+mONZ3q5dOwAQ509SS05OxrVr1wD8N3fl9evXtX7XHz58wP379wGYdghAIHVYVn2TBWte8+fPn2f5mMY+H4oVK4bWrVsDSM2XBY1eV0BqDw51ntGlSxdxbiHN47Ru3VrvMGT169cXew7v27cv3W9VLSut92UyGRYuXKhz/qs5c+aIwxtmtSWz+p4vXbq03u+latWqYp7z7Nkzva3is0Od51WrVk3slZpWxYoVxd/q8ePHtVrDqvOfatWqifPYaSpYsGC2euqpGVtmVH8PNjY2ep+zw4YNwxdffIHmzZtnOHSOKcXGxmLEiBHo1q0bJk6cqHdoFXPmY+qeFYGBgemGzgIg9gr74osvTD5Mj4WFhTgKxJkzZxAXF5duG/V326xZM626gCnrGab0/Plz8d4dM2aM3hbNo0ePFlve79mzR1xuTN3LENmp+xibj2jq1KlTtub9CwwMFOfGHT58uN7f5Pjx42Fvbw8A2L17t97jzZ8/P90wjAAwduxY8fvRrCcZ8/18jOuXF+8pU9bTihcvLo6+osnS0lLsxRETE5Mj+UFOPedPnTqFhIQEAKl1MV3lklGjRukcit2Unj17JpZz084Lq3br1i08fvwYADBlyhTY2dml26ZevXo6v6O05HK53vkRja3HmbIe2K5dO9SsWTPd8qpVq4rfSVbeIWlSl7lsbW0xefLkdOtlMhnmzZunt0yR0+8R1flgVFSUVs9TtWbNmmH9+vVwc3PLcI5Lzd/RzZs3s/z55mJvb6/zfYbmEJxdu3bVWT9SvytJTk7Wyn88PT2hUqkgk8kwa9YsnUOr/vTTTwa/79Ys52rWp3TRXJ+Vd2uUezBYlUtp3kjZHZJC3zjOuYF6nolmzZqhaNGierdr3769mMGknZvCWPXq1RMLRDNnzsT8+fPh6+urNdSXvb09mjVrli5T/uabb/Drr7/qnYw6NjYWfn5+4kM27XwkmvTNTaRZANI3Fqt6mLmMuharh2DRRfMFYlaGi9Qc5qlGjRqIi4vT+Z9EIhHHq9WckNaUGjVqpLOQCADW1tZo1KhRus/XTL96KAtdChQoID6s7927l64yVLRoUSxZsgQSiQTv37/HiBEjkJKSgmLFimH58uWZjoWb0Xei/tzExESxwpoR9TnZ2NiIQ1TpUrJkSbGwYci9pL6OycnJ6Nu3L9asWYObN29qvSht0qQJGjRooPf70OXKlSviMTJ7uap+QZuSkpKla2IoCwuLDAub6u/kzZs34stvY9P/xRdfiHnfypUr8dNPP+HEiRNa8+RUqFABLVu2ROXKlQ0aX1n9/VavXl2c+0jXf5ovQdLOk6P25ZdfZvlz036+KfL37Hy+odRDRYWFhaUbyvLixYvi8H26hgBUa9Cggc7lmvMGaOYjEokEEyZMwJIlSzBs2DCd+3748EFrGBVdFaacpg5WqVQqrWfEjRs3EB8fjxIlSqBv374AUocP0hxK0dfXF4IgoHDhwhmOh58dVatW1fvbKlmypPhvXS+y9DHF80H9W3r79m26vOrChQvi/HSav6WnT5+KQzzWqlVL7/0aFxcnDhUUHR2dbm4ltazcMw4ODnrHh1dPlKxOc2aio6PFoKSTk5POF01qmnNAmKpMFx8fj7t37wJInTssK9dPpVKJ309sbKy4f0bP0Pr16xs8d6CasWVG9ffQvHlzMZCYlpWVFY4dO4bt27dnONeGKZUqVQrTpk3DypUrdb78FQQBT58+FV/uARmXh3NCjx49xBdZmg1OgNTrfubMGQAZ5+/GUOcJCQkJOHnypNa6x48fi/dO2s83ZT3DlDTzyZo1a+q91xITE8X7Td2wATCu7mUIQ+s+xuYjaWW37KJ5fTMqW0okEvE+DwkJSTdvKgB8/vnnOl8yA6kNPtSNKzSfY9n9fj7m9ctr95Qp62l16tTRGwTVLG+a+oVsTj7nL126BCA1eKIvOCuRSAyeX9tQmvdQ1apVdW6jng9UIpFk2AgrK42GKleurPd5bmw9zpT1wIzm0FaXidTBxqxS57tNmzbV+56zdOnSqFOnjs51Of0eUX0fPnz4EP369cOOHTvEIbyB/36PtWrVyrBBZbFixcR3I7ry6NxG33etGWDX905S8zpoBpLU3/WXX36pVT/TVKhQIYPnstV8L2LI3IKcrypvyb3RjHxOM7qcdpLvrFK3DjHVZPRZkZiYqLe1LwAULlwYsbGx4qR8X3zxRYbHk8vlqFSpEh4+fIi3b99mKQ1KpTLDQpqFhQUsLS1RsmRJzJo1C7///juSk5Ph6uoKV1dXWFhYoH79+mjRogXat2+vsweMmkqlwpUrV3D//n28ePECr1+/xvPnz/HmzRutVtVpW1hr0veCXzMz1fcgzEqrPX2tgwCgSpUq4r+zMheDZs+AjF7ua8ruHA+Z0VeQVKtUqRJ8fX0RGhqKpKQkFChQQExL8eLFMyzcaB5fqVTi/fv36T6vVatWGDp0KHbu3ImUlBRIJBIsXbpU74NYrXDhwhmOravZciwr1069TZUqVTJ9WFetWlV8Yaq+JplxcnJC586d4e3tjZCQEKxfvx7r168XX2i2atUK7du3N3gyX81zy+y71MwnspoPGKJy5coZNgpI+51UqlTJ6PQXKFAACxcuxNSpU5GSkoKjR4/i6NGjkMlkqFWrFlq0aIF27doZ3NI7Li5OHI/Z399fbxAlLX3XNbP7JC1T5++Gfn52tG/fHra2toiMjMSRI0e0XiaoW+hWrVpV73chlUr1plMzj9b3bExISICfnx8ePXokTrT99OlTMbBhTuXKlUP16tURGBiICxcuiBVwdeWjWbNmKFWqFD7//HO8ePECly9fFnsFq8cfd3R0NOk8MEDGvwvNZ6dKpcryMU3xfGjdujWKFy+O0NBQHDlyRKuHtPq3VKlSJa37UvO5qs5fs+Lt27c6X+hl5Z7JqFwApOZ5vr6+CAkJgUKhyLDx07t378QyTmZ5YalSpWBjY4OYmBiT5eWvX78WXyq6ubnBzc0tS/upPz84OFj8nXz++ecZ7lOlShW9PWGzIjtlxqSkJLGVaqVKlbL92TktODgYly5dwrNnz/Dy5UsxH0sbMM6oPJwTSpYsiZYtW8LX1xeenp6YPn26WFby9vZGUlISZDKZwXNdZlWNGjVQq1YtBAQE4OjRo1pz6Gj2mHNyctK5vynqGaakmV+NGjUqS/vExMQgJiYGNjY2Jql7ZYWhdR9j85G0slt20SxbZlaG0lz/5s2bdPmv5nnqos5PNJ9j2f1+Pub1y2v3lCnrafp64QPajZsNKftkRU4+59XXJ7Pnb2afayzNe0/fuzN1D6ISJUpkGKTI7N4F9P/Gja3Hfcx6oPo3Z8i9kpiYKL6jzMp3nnbeqpx+jwgAkyZNwsWLF/H06VM8ePAAv//+OwCgTJkyaN68ORwdHeHo6CiO4pORIkWKICwsLMNRkDRl9T1mTtCXvxjzTlJ93qa+vzWvfWajCWhez6y8+6Lcg8GqXEqzEPjo0SODX1jGxsaKD9ScfrhrGjt2bIY9aQIDA7UqrrqGTUpLnRlpturKyLVr18QhBnSZPHkyvv/+ewCpw6bUqVMH27dvh6+vLxISEpCSkoIrV67gypUrWLVqFerXr4/ffvtNHG5B7ciRI1i/fr3OYYZKlSqF5s2b48yZM+IDVR99rWo0GdJiIC117ytdNLvFqocoyEhWtjHFPlmRWQFB87omJCSgQIEC4hBsWfndaW6j77fXtGlTsau9TCbLUs+ijL6PtOnOypB3hpyT5jWLi4vL0gNbKpVi9erV6NChA1xdXXH9+nWoVCpER0fj1KlTOHXqFBYsWICuXbti7ty5KFKkSKbH1Ew3oL/goyvdWc0HDJGd78QU6XdycoKHhwf+/vtvnDp1CtHR0VAqlbhz5w7u3LkDZ2dnVKtWDb/++qvWS++MZHeYRH33qaGFOlPn7x+jUGlpaYmuXbti9+7dOH36NOLj42FlZYW4uDicOnUKQMat7uVyebby6JSUFGzYsAF79uxJN8SmRCIRh6XK6kufnNK+fXsxWKWm/re6N0XTpk3x4sUL+Pv7Y/LkyUhOThZby+ZEa9icqKiZ4vkgl8vRvXt3bNu2DSdOnMAvv/wCS0tLxMbG4vTp0wD+axWult1npL57PSv3TGZ5lrpsIAgC4uLiMszXNdOR1Xs+JibGZHl5dq+fej/NMlpm5bHMnhUZyW6ZUTNvyEp58WMLDQ3FsmXL4O3tna73p/oFd5EiRfDvv/+aJ4FIved8fX3x7t07XLlyBU2aNAHw34vtFi1aZNrQyNjPDwgIwKVLlxAaGorixYtDEARxWKK0w4KqmaqeYUrG3G/q+8eYuldWGVr3MTYfSSu7ZRd1fiqXyzM9RmZl48zyY839NV/mZef7+djXLy/dU6asp5lr1JycfM6rG2Xn5PM3KzTrL/o+S91wJLO0ZiWIoa8ca2w9ztT1wIx60WWHIWUuXWXVnH6PCKQGbQ4fPowdO3bAw8ND7B3+7t07HDp0CIcOHYKVlRXGjBmDiRMnZlgHVP+Wsvq9GPIe09Sy8rs1tL6b1fvb0CH/DenYobk+o4A/5T4MVuVSzZs3h0QigSAI8PPzQ58+fQza/+LFi2Ir7ubNmxv8+YZ25zVE2oJYZtSZe05V0uvVq4e//vpLnIPD398fly9fxp07d6BSqXDz5k0MHz4cXl5eYga3b98+zJs3D0BqZtmhQwfUrl0bVapUwRdffCH2MnF0dPyolUhdEhMT9T7MNR/cWcm81QX/YsWKiS8izSWz36j63CQSiVhQUP/2DPndae6nKTw8HL/++qv4t0KhwIwZM3D48OEMx87NbGgGze8kKy0zDTknzW2yUiDR1K1bN3Tr1g2RkZG4fPky/P39cfHiRTx//hwqlQpHjx5FcHBwlufD0vz82NhYnfNFaa5Xy4l8IDvfianSX7VqVSxZsgQLFy7E7du3cfnyZVy+fBk3b95ESkoKHj16hG+//RaHDh3KUks9zd/et99+a9A8YqaQ2/L3rOrVqxd2796N+Ph4nDp1Ct27d8fJkycRHx+fY63uZ8+eLc6HVb58ebRv3x41atQQnyPW1tZ48eKF2YNV7dq1w4YNG/Du3Ts8efIEdnZ24nAw6uH9mjdvjn379uHWrVtITEzE1atXER8fDwsLC3GoodzOVM+H3r17Y9u2bYiMjMS5c+fQoUMHnDhxAomJiZBKpejRo4fW9pq//c2bN3+U65VZK0R1nieXyzMdR16zfGGOe17zOPPmzdM7V4o+mi2oM3uZkd25oIwpM2qen7nG2tdX3oqLi8OQIUPEF79169ZFy5YtUa1aNVSuXBlVqlSBpaUlDhw4YNZgVbt27cTes0ePHkWTJk3w5s0bcXi6nBoCUK1bt25YtmwZkpOTcezYMYwYMQL+/v5iS35dn2/ueoa+71yzjHHz5k2Dy5Jq2al7GcLQuo+x+YipqK+nQqHIdASEzMrGmdWTNPPrtPm8od/Px75+eeme+lj1tJyUk8959TM4p56/WaX5El5fLxF1/pdZWo15j2ZsPc7c9cDMaL7byM53/rHqmQULFsS4ceMwbtw4vHr1CpcuXYK/vz8uXbqEsLAwxMfHY82aNVAqlfjhhx/0HkfdKym/Dj9na2uLkJCQTL/rjKY00aVMmTKwsrJCfHw83rx5k+G26l51VlZWDFblMQxW5VKlSpVCixYt4OfnBx8fH7x69cqgsbu3bdsGIPVFQ9pxu9WZZUbzX2R3Ys6svKi2sbFBkSJFEBUVpTWOvS4pKSnikBPqiWAz06RJE71zOWTE0tISzZs3F4N7oaGhWLJkCTw9PREREQEPDw+MGjUKSUlJWLlyJYDUjPLAgQM6h0BTqVTpWsubw8uXL/WOWa45/m5Wrq96m/DwcHz48CHDlqiCIBjVIywzmT2Y1GMDly9fXix0qoffCw0NRURERIbBoEePHgFILbxqTtCrNm/ePISEhEAul2PKlClYsWIFnj59iuXLl4uVIV0iIyMRFRWlt6W6od9J+fLl8eDBAzx9+hQqlSrDwpD6nOzs7DKdjFIfW1tbfP311/j6668BAEFBQZgxYwYePnyIK1euICAgALVq1cpSutWePHmSYQ9QdbqBrOcDhnj16lWGv1fN70SdblOnXy6Xw8HBAQ4ODpg0aRJiYmKwfv16bN++HYmJiXB1dcUvv/yS6bl89tln4vAb2ckHjZXT+XtOqVOnDuzt7REUFISTJ0+ie/fu8PHxAZAaiMkoGJkdt2/fFgNVrVu3xvr163W2XtQ3+fjHVLt2bZQqVQrBwcG4cOECSpUqBZVKhc8//1yc96hp06aQSqVITk7G9evXxRfTjRs3zvHWsKZiqudDtWrVUKdOHdy9exfe3t7o0KGD2Nq7efPmKF26tNb2mnNHPXz4MMNglameq5pDeemizvPKli2b6eeptxEEIdMx+d+9eye+XDDVPV+mTBnx8zPL83Rdv1KlSkEqlUKlUmnl9bq8evXK4PQZW2a0sbGBtbU1YmNjdfYG0LRnzx5ER0ejevXqaNu2bZbSpz737NQJdu/eLaZp6tSpGD9+vM7tzJ2PWVpaolu3bti1axdOnz6N3377DSdPnoQgCPjss88ynHvEFGxtbdGuXTscP34c3t7eGDFihJgnVK5cOd08ETldzzCmHqh53wYGBmY4n0lW8qus1r0MZWjdx9h8xFQ0y5aPHz/OsDytWbbUNbx4VutJBQoU0FvGyer387GvX267pzLysetpOSEnn/NlypTBrVu38OzZswx/Gy9fvjQ84QbQ7Nmh78W6eiiz0NBQxMbG6u0N8uzZs2ynw9h6nLnrgZmxtLSEnZ0dwsLCslXmMkc9s0KFCqhQoQL69+8PlUqFc+fOYcaMGYiOjsY///yTYbBK/VvKSi8wIPvvMXOrMmXKICQkJNPv2tD7WyKRwN7eHrdu3UJAQECG26rnU6xRo0aOvpsk08ufId484scff4RUKoVCocBPP/2U5Yjz1q1bcfPmTQBAnz590k2irW5ZkFFASnNS97RMcZM3atQIQOqkmhml4/Tp02KriqyOuZtVW7ZsQffu3dG+fXudY+0WL14cc+fOFf9Wj7n66NEjscVV586d9c7Vc+3aNTHtH3uMfk3qeUN0UU9MK5PJ0KpVq0yPpZ5wEvhv+BRdEhIS0KpVK7Ru3RqzZs0yILVZd/36db2trMLCwsThKDXTrP7dAalzFeiTlJQkTrpds2bNdK3bDh48KF67cePGYezYseLQTrt37xYnYNUnoxbG6uPa2tqKkxBnRH1OMTExOH/+vN7tPnz4IE5knPZeyuienj59Ojp27Ijp06frXG9vb49JkyaJf2d1TOYGDRqIQUQvL68Mt1V/VzKZLEvXxFDR0dF6J3kG/vtOatSoIb5oNjb97u7u6N27N5o0aaJz6AUbGxvMmjVLrAhl9bpKJBJxyEB/f/8M9/P29ka9evXw9ddf4/jx41k6flbkhvw9O9Qtcf38/BAZGSnOy5R22DZT0Py9DRo0SO8wG5pD76Wdg+BjFbglEgnatGkjpkedt6qHAARS8yv1i8FLly6Jzx1DXwKbsxJhqucD8N9v5t9//8Xbt2/Fa6artXeNGjXExgtHjx7NsLywcOFCNG7cGD169DDqxY2/v7/elzFRUVHiRNvq7z0jn332GWrUqAEA8PHxyfAFuGZeaap7vmjRouLQ2T4+Phm2aN66dSsaNGiAbt26ifdg4cKFxTzz1KlTeueWe/bsWaZBPl2MLTNKJBI4ODgASL239NUFBEHA2rVrsWrVKq3emJndU8bUCTSXDxs2TO/+GeVjH4v63gsPD8eNGzfEYTm7dOnyUYabVecJt27dwps3b8TGELryhJyuZxjznWe1HiAIAnr06IGWLVti9OjRYvqyW/cylKF1H2PzEVPRvL4ZlS0FQRDLbba2tlpzq6oFBQXhw4cPOvdPTk4Wn2P169cXhyTL7vdjjuuXm+6pjJiinmZuOfmcVzfQiYqKwuXLl/Vul1nd2liaDY+Cg4N1bqMeTUAQhAzr8hnlP5kxth6XG+qBmVF/5xcvXtQ7PF5MTIz4PjOtnKxnvn//HiNGjECrVq2wd+/edOulUinatGkjjpIQGxub4RB/6t9S2vex+YX6u3748KHeMrRSqYSfn5/Bx1Y3yrp7967ee/b58+cICgrS2p7yDgarcrGvvvoK3333HYDUgti3336b4YTrKpUKGzduxB9//AEAqFixIn766ad026knVH306JF482rSbBWti+Z4yRkVVjIyaNAgAKnDNMyfP1/ny4HQ0FAsX74cQGq3zbQ9xIxVoEABBAYG4vXr1zhy5IjObdRDHQH/tabRPH99LTrCw8OxYMEC8e/sXidT2L59u86WKQ8fPsSuXbsApL5QzEq32LZt24qFOWdnZ70tP1avXo2QkBC8f/8+S0OXZUdkZCRWr16dbrlKpcKCBQvEaz548GBxXZs2bcTCwl9//aW3lfTixYvFwk///v211r169QqLFy8GkBqomTBhAoDUIb3UlZ///e9/GbYk/uuvv3S23PPz8xMLi7169crS2OQ9evQQX5YuXrxY5+cqFArMmzdPvCZpz0nzc9K+CEtKSsKLFy9w5swZva1iNFu0ZHUC+KJFi6Jz584AgOPHj4tzA6V17NgxMT/q1KlTlufEMtSyZct0Bj8PHDiAO3fuAAD69u0rLjc2/TY2NggICEBkZKTeHqkvXrwQWyemnZhUHdzQlbeof/MpKSn43//+p/PlZkREBFavXo2EhAS8efMGdevW1ZmG7MgN+Xt2dO/eHXK5HPHx8Vi5ciUSEhJyrNW95hAj+p4jd+/exd9//y3+nfa7NsWzOKvU1+DKlSvii2d1pV1N/bebmxvevHkDiURi8HxVH/Oc0jLF80GtW7du4jyJixYtglKp1Dvhu0wmE4/z6NEjrF+/Xucx79y5gwMHDiAqKgoKhcKg3vZpxcbG4s8//0y3XBAELFq0CElJSZBKpVkO1Krv+eDgYKxYsULnNk+fPoWzszOA1JdCaRvIZJSnZbZenedFRERg4cKFOl8yvnr1Cn///Tfi4uIQFham1euiX79+AFJ7IqjTqEmpVIoTbBvKFGVG9e8jKipKZ7kHAP755x/x+f/NN9/o/HxdzwL1M/vy5cs6yw+enp56y3qa+Zi+1va7d++Gv7+/+Le5ysO1a9cW59c5dOiQGIzJ6SEA1Vq2bImSJUtCEAT8/vvviI6OhlQqRc+ePdNtm9P1DGPqgbVq1RLLCwcOHNA7JPjOnTsRGBiIkJAQVKxYUQyaZrfuZajs1H2MzUdMoUaNGmKvoB07dojlz7S2bNkifnf9+vXT2VtHpVJh0aJFOs9j5cqVCA0NBaBdTzLm+/nY188c91RmzyldTFFPyw1M8ZzXpUOHDuJQgIsXL9b54v/o0aN6AxemojlChr6X6i1bthTLXn/99ZfO4SKfPn2Kf/75x6i0GFuPM3c9MDPq6U3i4+P1lq2WL1+uN+htinqmvnu5RIkSYqB/z549OtMgCAIePHgAILUXpL4edlFRUeL7noxGYPmU9ejRA5aWlhAEQev9nKbNmzeLQ7gaonv37rCwsIBSqcTixYvTPXOUSiUWLVoEILXn5IABA7J3EmQ2DFblcj/++KPYWvHKlStwcnLCsmXLcPXqVQQHByMiIgJBQUHYvXs3evTogVWrVkEQBFSsWBF///23zrkGOnbsCCA1ox0/fjxOnTqFsLAwPHv2DM7Ozhg9enSGw/Zoju9/7NgxfPjwweAhPlq2bCk+NE6cOIERI0bAz88P4eHhCA4OhpubG/r27SsOYbBw4UKTjzHau3dvMbgwd+5crF69Gg8fPkRERASeP38OV1dXsTdJ0aJFxTlLqlWrJvauOHfuHBYsWICgoCBERETgyZMncHFxQY8ePbQKw1kZUzenREdHY9CgQTh8+DBCQkIQHByM3bt3Y/jw4UhMTIS1tTX+97//ZelYFhYW+O233yCVShEXF4fBgwdjy5YteP78OcLDw3H79m1Mnz4dLi4uAFKvVdrWtsHBweIwcsaMoyyTybBt2zbMmjUL9+/fR0REBG7cuIGxY8fixIkTAIChQ4eidu3a4j5yuVxMf2RkJPr374/du3fj9evXiIiIwLVr1zB+/Hi4uroCAFq1aoWBAweK+yuVSsyYMQNxcXGQyWRYsmSJ2BqxSJEi4hxWISEhGQ4F+ObNGwwYMAAnT55EeHg4Xr16hU2bNmHixIkQBAHlypXL8uSZtra2mDNnDoDU1iN9+/aFu7s7goODER4ejnPnzmHYsGFiK68BAwbA0dEx3THUDh48iPDwcLG3z9ixYyGVShEfH49Ro0Zh//79ePbsGSIiIhAYGIiVK1diy5YtAFLva0MKYzNnzoSdnR0EQcCPP/6IP/74A0FBQYiKikJgYCCWLl2KGTNmAEgdqkmzNaep3blzB0OGDMHFixcRERGRbkjHOnXqaFXojU1/27ZtUb16dQDA2rVrsXDhQty7d0/8PRw9ehTffvstBEGApaWl1u8Q+O87CwwMxLVr1xAeHi4G2xwdHcX81c/PDwMHDhTz+Xfv3sHLywuDBw8Wh28aP368zqEusys35O/ZUbx4cbFifeDAAQCpLXBzotV9y5YtxZdL69atw44dO/Dy5UuEh4fjzp07WLZsGQYPHqxVSUrbE8bW1lZ8Aejj44N3796JL59MrVmzZuLY4M+ePYNEIkGTJk3SbQNAbFRTq1Ytg4dP1MyL3N3dERYW9tGG0zX2+aBJPR8GgCz14Bg/frzYMn7t2rWYMmUKrl+/LpZHtm/fjm+//RbJycmQyWSYN2+e0b3Qdu7ciZ9++gn3799HZGQkbt++jQkTJogvKEeMGJHlF4l9+vQRW7q6uLhg8uTJuHbtGiIiIvD69Wvs2LEDgwYNQkxMDGQyGVasWJFucnH1d3/16lUEBgYiLCwMCoUiS+v79esnfv6hQ4cwevRoXLx4UcxPDxw4gCFDhohBxlmzZmn1iOvevTtatmwJIPX6/+9//8PDhw8RGRmJa9euYfTo0Th//rzeeSwyYooyY/v27cVeblu3bsWMGTNw584dRERE4OHDh1i2bJnYUK1x48ZaQdGMnu/Af3WChIQEjBo1CpcuXUJ4eDiCgoKwbNkyzJw5U28jEc2yxNSpU+Hj44Pg4GBxyNAff/wRv/32m9Y+hkxybmrq4Ovhw4ehUChQuXLlHOmtrYtMJhNfoqvzBH1DzOZ0PcPYeuCCBQtQsGBBKBQKjB07FqtWrcKjR48QERGBBw8eYNGiRWKjrhIlSmiVZ7Nb9zJUduo+xuYjpvLrr7+iYMGCSE5OxogRI7Bx40Y8f/4cUVFRuHPnDubMmSMOaVejRg299QWZTIYTJ07gu+++w82bNxEREYH79+9r1dPatm2LTp06ifsY8/187Otnjnsqs+eULqaop+UGpnjO62JjYyPWj4KCgjBo0CCcPXsW4eHhePHiBf766y/Mnj07W89fQ1SqVEn87d+7d0/nNlKpVEzry5cv0b9/f5w4cQJhYWEIDg7Gvn37MGTIEK3fTnbKasbW48xdD8xMw4YNxcCBm5sbJk6ciDt37iAyMhL37t3DlClTsH//fr3fuSnqmfrq0TKZDGPGjAGQ+nscMWIEzpw5g7dv3yI0NBTXr1/HDz/8IM55OWLECL3nqdmYV3P0hvykQoUKmDx5MoDUXvbq+QUjIyPx6NEj/Pbbb1i9enW27u+yZcuKHTuOHz+OCRMm4Pbt21rvBNW9WSdPnpxjDZ4p53DOqlxOIpHgl19+Qf369bF06VJ8+PAB27ZtE+ekSksmk6FXr16YMWOGVgVVU9euXcWeAG/evNEawgtIrVisWLFCb+b7+eefo1y5cnjz5g3c3Nzg5uaGChUq6O1ZoM/ixYshl8vh7u6Oq1evisPOaLKyssK8efNypNW9tbU11q1bh++++w6RkZFwdnbW2aK2WLFi2LRpk9hqQiaTYfHixRg/fjySk5OxZ88e7NmzJ91+Dg4O+Oyzz3D27Fm8evUKSqUyxwtauvTs2RMeHh6YPXt2unWlS5eGs7Nzujk0MuLo6Ig///wTP//8M2JjY7FixQqdLaxq1KiBTZs2pRtzOyUlRRzLWd8wDFnRr18/3Lx5E+7u7nB3d0+3vk+fPmLlQFOrVq3E9IeHh6d7kaLWrVs3LFiwQKuQuXnzZrFl15gxY7QCYUBq67AuXbrAy8sLJ0+exMGDB7V646j17t0bbm5u4sNbU7Vq1eDs7JzlsY2B1BZ4iYmJWLZsGd68eaNz6EWJRIIRI0boHM6vcePGkMvlUCgUWLBgARYsWIA+ffpg8eLFqFu3LubOnYtFixbh/fv3egNGtWrVEl+WZVWpUqXg4uKCSZMm4eXLl/j777+1epJoHvvPP//MsYCGhYUFOnXqBE9PT51zIzRq1Ahr1qxJd/8ak36pVIo1a9Zg9OjRePPmDXbt2iW29tVkZWWFlStXputF0bx5cxw7dgzx8fHiRNbLli0TK+6a+WtAQEC6fB5I/U2MGjUKEydOzPwiGcjc+Xt29enTB2fPnhVbZ+VUq/uqVati0qRJWLt2LRITE/H777/rbF3Yv39/XLp0Ca9evUo3Bn6BAgXg4OCAa9euwc/PD23atIGlpSVu3ryZpV6ZhrC0tESLFi20hsRMez82bNgQlpaWYgtOQ3tVAalBYfVY+xs3bsTGjRvRtGlTo1upZpUxz4e0evfujWPHjmn9rY+1tTW2b9+OSZMmISAgAN7e3jqHIixUqBAWL16cLlBoqFq1aiElJQVHjx4V5/nQNGzYMDHQnhVyuRwbNmzA1KlT4efnh5MnT4q/FU3FihXD0qVLxSFqNDVv3hy3bt1CcHCw+AJ09+7d4rYZrU/7+RcvXhSH8UybzhkzZuhsef/XX3/hxx9/hJ+fHw4ePIiDBw9qrW/bti0UCkWGwzjpYooyo0QiwcqVKzFt2jT4+vriyJEjOns9NGjQAOvWrdP6XWb0fAeA4cOH499//8XNmzfx8OFDjBw5UuuYlSpVwowZM3Q+Q/r06QMfHx/4+fnh9evXOl+aFyxYEGPGjMG6desAINN5t3JS9+7dsXLlSrFl78fqVaXWu3dvbN68WetvXXK6nmFsPbBmzZrYsmULfvzxR4SHh4t5dVrlypWDs7Mz7OzsxGXZrXsZKjt1H1PkI6ZQo0YNreu7atUqrFq1Kt12LVq0wLJly/Q2gmjfvj1iY2Ph6+urcwi11q1bi0EvNWO+H3Ncv499T2X2nNLH2HpabmCK57w+3bp1Q1RUFH7//XcEBQWlm//Q1tYWw4cPx5o1a4w+j4y0atUKbm5u4tDNurRu3Rpz587F4sWL8fz583TzFclkMgwaNEgcQi67736MrceZux6YmV9++QVJSUlwd3fH6dOnxYCzmro3tOawxpqMrWdmVI8eNWoUAgMD4eHhITbk0qV3794YO3as3nNU9yovUqTIR+25ltuMGzcOsbGx2Lx5M65fv47hw4drrS9fvjw6duyo9/12RiZOnIg3b97g8OHDOHv2rM4hOEePHp2tuS/J/BisyiO6du0KJycneHt74/Tp07h//z5CQkKgUChQpEgRVK1aFc2bN0f37t0znUBQKpVi3bp18PDwgJubGx48eICkpCSULVsW7du3x5gxYzIcp1kmk2HLli1YunQpbt26hcTERAiCgKSkJINaoBcoUADLli1Dnz59sH//fty4cQOhoaEoWLAgypcvj3bt2qFv374GBVIMVa9ePXh5eWHXrl04f/48nj9/jvj4eNjY2KBSpUpo06YNhg4dmq6FYYsWLeDm5oa///4bV65cQUhICCQSCYoXL47q1auje/fu4vi/Z8+eRXR0NM6fP5+l+R9MbdCgQejXr58YaFEqlahUqRI6d+6MAQMG6Ox9l5nOnTujUaNG4nV7+fIl4uPjYW1tjZo1a6JLly7o1auX3nlYTMHGxgb79u3D5s2b4eXlhbdv36Jw4cKoV68eBg8enGGrtM6dO6Nhw4bYtWsXzp07h5cvXyIlJQWlS5dG3bp10bdv33QvA+/duycOz/TFF1/oDDQBqS0R1WMo//7772jSpEm6QMO0adPg5OSE7du3IyAgAHK5HFWqVEGPHj3Qq1evbE2qO3z4cLRp0wY7d+7EpUuXxNZEpUuXRuPGjdGvX790wTW1KlWqYN26dVi3bh2ePHkCiUSiNQzD4MGD4eDggN27d+PatWt4+/YtFAqFOE/N119/jZ49e2arQG5vbw9PT08cOHAAPj4+CAoKQmxsLOzs7FCtWjX06NEDnTp1ylLrvOxSvwhs0aIF9u7di0ePHqFQoUKoVq0a+vXrh86dO+t9+W9M+itVqoQjR45g7969OHv2LJ48eYKYmBgULlwY5cuXR6tWrTB06FCULFky3b59+vRBREQEDhw4IP72NXu4qvPXfv36Yf/+/bh+/TpCQ0OhUqlQsmRJNGrUCAMHDsyxVuW5IX/PjjZt2qBYsWIIDw/XOUm3KU2ePBl16tTB7t27cefOHURHR4sTnderVw8DBgxA/fr18euvv2Lv3r1ijwfNINGqVauwePFiXLp0CXFxcShWrBjevXtn1BBx+rRv3158MaE5X5VawYIFUb9+fbFylp1gla2tLbZs2YKVK1ciICAAKpXqo/fEyM7zQZfmzZujTJkyePfuHapUqZLpvaae9N3T0xPe3t64d+8eIiMjYWFhgQoVKqBFixYYNmyYSca9L1q0KNatW4ctW7bg2LFjePv2LYoXL46vvvoKw4YNM+glk9pnn32GrVu34vTp03B3d8ft27cRHh4OGxsbVKxYEZ06dUKvXr1QtGhRnftPmDABycnJOHbsGEJDQ2FjY6M1Bn1m69Wff+bMGbi7u+POnTsICwuDVCpFmTJl0LRpUwwdOlTv0MTW1tbYvHkzjh8/jn379uHRo0dISEjA559/jt69e2Po0KHZfqFjijKjOn2nTp2Cm5ub2Aq5UKFCqFGjBrp3747evXunew5n9ny3srLCjh07sG/fPhw9ehSPHz+GIAioUKECOnfujJEjR+odNksul2PTpk1wdXXF0aNH8ejRIyQmJqJw4cKoUKECmjZtikGDBqFChQrw9vbGkydP9DYK+Rjs7Ozg6OiI06dPQyqVinNOfCzqZ8rNmze1el/qkpP1DGPrgUBqEPTkyZPYs2cPzp49i6dPn4rll2rVqsHJyQkDBgzQ2XMmu3UvQ2S37mNsPmIqjRs3ho+Pj9b1jY+PR8mSJVGrVi306tULbdu2zbDBhFwux5YtW7Bz5064ubnhxYsXKFiwIGrWrIl+/fqha9euOvc35vv52NfvY99TmT2HMmJMPS23MPY5n5EhQ4agYcOGcHFxwbVr1/D+/XsULVoUjo6OmDx5Mm7dumX6E0rjm2++Ee+VZ8+e6ZwLDkgduaVBgwZwcXGBv78/wsLCYGNjg0aNGuG7775DYmKiGKzKTp0eML4eZ+56YGYsLS2xbNkydOnSBbt378aDBw8QFRWFcuXKoWvXrhg7diyWLVumd39j65kZ1aOlUimWL1+OLl26wM3NDXfv3kVoaKiYXzRo0AB9+vRJNxx6WupGAp07d87Rd2K5nUQiwfTp09GmTRv8888/4vUsWbIkOnTogEmTJokjmhhKJpNh6dKl6NixI/bt2yfWp21sbFC3bl0MHjxYnDeL8h6JYKrZI4ko13BzcxN7Fe3bt89sBRH6z9q1a8WWxX5+fkb1KiPTmD17Ng4fPgxLS0vcvXvX3MmhXEChUMDR0RFhYWGYNm2aOLwA0aegXbt2ePPmDVq2bImtW7eaOzlEH933338PHx8f3gOfINZ9iMgYgiCge/fuCAoKwuTJk7M8JH9avr6+GDduHICs9byjT8/jx4/RtWtXSKVSHDlyBNWqVTN3kojyHM5ZRURERITUQHJYWBjkcnmODfFDREQfX2RkJP79918A0DlEMxER5V8SiUQcLs/NzS3dfGRBQUGYNm0a1qxZk2GPujt37oj/rlKlSs4klnK1/fv3A0jtVcVAFVH2MFhFRERE+V5SUpI470b79u11TtJNRER5k7OzM5KTk1GiRIlsDVNKRESftk6dOqF69ep4+/YtfHx8tNYVKVIEx44dw/r16/UOW/bhwwe4uroCSJ13KafmW6bcKyYmBgcPHoRUKtU5VxgRZQ3nrCIiIqJ86e7duzhz5gwKFSqEEydO4N69e5BKpRlOmEtERLlfbGws1q9fjxIlSuDWrVs4ceIEAGDMmDH5ev4IIiLSTSKRYPbs2Rg9ejTWr1+Pr7/+GlJpavv+UqVKoVmzZrh06ZLY+KFLly4oVaoUoqKicPPmTWzYsAGhoaGQSqX4+eefzXw2ZA7btm1DXFwcBg4ciKpVq5o7OUR5FoNVRERElC8lJSVhw4YNWsvGjBmDOnXqmClFRERkClZWVti5cydSUlLEZQ4ODhg2bJgZU0VERLlZ8+bNMXToUOzcuROHDh1Cv379xHVLlizB6NGj8fTpU2zatAmbNm1Kt7+VlRUWLlwIBweHj5lsygU+fPgAFxcXfP7555g1a5a5k0OUpzFYRURERPnS559/jsqVK+PNmzcoU6YMBg8ejJEjR5o7WUREZCSpVIoWLVrg0qVLsLGxQceOHfHTTz9BJpOZO2lERJSLzZgxA5cuXcLKlSvh5OQEW1tbAECZMmXg4eGBAwcO4Pjx4wgKCkJsbCw+++wzlClTBm3atEHfvn1RtmxZ854AmcWyZcuQlJSE5cuXw8rKytzJIcrTJIIgCOZOBBEREREREREREREREeVPUnMngIiIiIiIiIiIiIiIiPIvBquIiIiIiIiIiIiIiIjIbBisIiIiIiIiIiIiIiIiIrNhsIqIiIiIiIiIiIiIiIjMhsEqIiIiIiIiIiIiIiIiMhsGq4iIiIiIiIiIiIiIiMhsGKwiIiIiIiIiIiIiIiIis2GwioiIiIiIiIiIiIiIiMyGwSoiIiIiIiIiIiIiIiIyGwariIiIiIiIiIiIiIiIyGwYrCIiIiIiIiIiIiIiIiKzYbCKiIiIiIiIiIiIiIiIzIbBKiIiIiIiIiIiIiIiIjIbBquIiIiIiIiIiIiIiIjIbBisIiIiIiIiIiIiIiIiIrNhsIqIiIiIiIiIiIiIiIjMhsEqIiIiIiIiIiIiIiIiMhsGq4iIiIiIiIiIiIiIiMhsGKwiIiIiIiIiIiIiIiIis2GwioiIiIiIiIiIiIiIiMyGwSoiIiIiIiIiIiIiIiIyGwariIiIiIiIiIiIiIiIyGwYrCIiIiIiIiIiIiIiIiKzYbCKiIiIiIiIiIiIiIiIzIbBKiIiIiIiIiIiIiIiIjIbBquIiIiIiIiIiIiIiIjIbBisIiIiIiIiIiIiIiIiIrNhsIqIiIiIiIiIiIiIiIjMhsEqIiIiIiIiIiIiIiIiMhsGq4iIiIiIiIiIiIiIiMhsGKwiIiIiIiIiIiIiIiIis2GwioiIiIiIiIiIiIiIiMyGwSoiIiIiIiIiIiIiIiIyGwariIiIiIiIiIiIiIiIyGwYrCIiIiIiIiIiIiIiIiKzYbCKiIiIiIiIiIiIiIiIzIbBKiIiIiIiIiIiIiIiIjIbBquIiIiIiIiIiIiIiIjIbBisIiIiIiIiIiIiIiIiIrNhsIqIiIiIiIiIiIiIiIjMRm7uBJBuyckKREUlmDsZRGZTooQNACAkJMbMKSGivIh5CBEZg3kIERmDeQgRGYN5CBEZI7fkIep0GII9q4iIiIiIiIiIiIiIiMhsGKwiojzt2rUruHbtirmTQURERERERERERETZxGEAiSjPSklJwcaN6wAAzs5bYWFhYeYUEREREREREREREZGhGKwiojyld+8u4r9TUlKgUCjE5WmDVW5uXh81bURERERERERERERkOA4DSER5kiAIYqAKABQKBQRBMGOKiIiIiIiIiIiIiCg7GKwiojwpJSUFANC6dWs4OjpqLSMiIiIiIiIiIiKivIPDABJRnqNUKqFUKmFpaYmpU6cCAC5fvozk5GQolUrIZDIzp5CIiIiIiIiIiIiIsoo9q4goTxEEQexBNWzYMJQvXx7ly5fH0KFDAaT2ruJwgERERERERERERER5B4NVRJSnKJVKCIKAUqVKYeTIkeLyUaNGoVSpUhAEAUql0nwJJCIiIiIiIiIiIiKDMFhFRHlGTEy02KtqypQpKFSokLiuUKFC+PHHHwGwdxURERERERERERFRXsJgFRHlGTt3umDTpk04d+4cOnTokG69k5MTGjRoAABiUIuIiIiIiIiIiIiIcjcGq4go1/HwcEPPnj3h6uoqLrO1LYh5835GvQb1YGVlBYlEAgBQCv8N+SeRSDBjxgxIpVIolUo8e/Y03XG//XYoPDzcPs6JEBEREREREREREVGmJEI+Gyvr2bNnWLt2La5fv47IyEhUrFgRAwYMwODBgyGVpo/dRUdHY9OmTTh16hTevXuH4sWLo2PHjpg8eTKsra1zLJ3JyQpERSXk2PGJPoaUlBS4ux8EAHTr1gNubgdw7txZxMXFIiEhIdOh+goUKAAvLy9I5VLIpXLEKGLgHuaOgPgAxCvjYSWzQi2rWuhp1xM2chsoBAXkEjmWL1+O/fv346uv6mHp0j8hkUjg4+ONjRvX6vwcS0tLVKnyBWQyGapXr4FHj4JQs2Yt9OkzABYWFia/LkSU80qUsAEAhITEmDklRJQXMQ8hImMwDyEiYzAPISJj5JY8RJ0OQ+SrYNXDhw8xZMgQxMbGokGDBrCzs4O/vz+io6PxzTffYMWKFVrbx8bGYvDgwQgMDETlypVhb2+PgIAAvH79Gl988QVcXV1hY2P4Rc8KBqvoU3D0qDu2b98MAGjSpBn8/S8ZtP/Zs2cBGRCWEoYNbzfgZMRJJAlJ6bYrICkAp6JOmFh2IopbFIegFNCpUydERUVhzpx5cHRsi969uxic/tGjx6Fbt54G70dE5pdbCmdElDcxDyEiYzAPISJjMA8hImPkljwkO8GqfDMMoCAImDlzJmJjY7F8+XLs3bsX69atw4kTJ1C9enUcPXoUJ06c0Npn9erVCAwMRP/+/eHl5YU1a9bgxIkT6NGjBx4/fozVq1eb52SI8oDo6Cjs379b/NvQQNXp06cBGfAs8RmGPRwGz3BPnYEqAEgSkuAZ7onhD4fjaeJTSGQSeHt7AwC2bHHGlCkTsnUOrq67EB0dla19iYiIiIiIiIiIiChr8k2w6sKFCwgMDETjxo3Ro0cPcXmxYsUwf/58AMDOnTvF5dHR0Thw4ACsra0xa9YscYhAuVyO+fPno0iRIjh48CDi4+M/7okQ5RGurrsQFxeX7f2lcinCUsIw6dEkhCnCsrRPqCIUkx5NQmhKKGRyGWrUqIHQ0BA8efI4W2mIj4/Hvn27M9+QiIiIiIiIiIiIiLLtkwhWeXh4YPDgwWjQoAG++uorfPPNN9i0aRMSExPFbc6fPw8A6NChQ7r9HRwcYGdnh+vXryM2NhYAcPXqVSQmJqJp06bp5qYqXLgwmjVrhsTERFy9ejUHz4wob3rx4jlOnPDK9v5nzpyBXCrHhrcbshyoUgtThMH5rTNkUhm2b98OAFAoFFCpVNlKy/Hjx/Dy5Yts7UtEREREREREREREmcvTwSqVSoXp06dj5syZuHfvHurXr49WrVrhw4cP+PPPPzF48GBER0cDAB4/Tu1ZYW9vr/NYlStXhkqlwpMnT7S2r1atms7tq1SpAgAIDAw06TkR5XWCIMDFZQuMmQ5PLpcjRhGDkxEns7W/T4QPYpWxsLCwQJcuqXNVpaSkZOtYgiBg+/bNRp0PEREREREREREREemXp4NVu3btgqenJypVqgQvLy9s3boV69evx+nTp9GmTRsEBASIQ/x9+PABAFCiRAmdx1IvDw0NBQCEhIRkafuwMMN6fRB96q5fv4Lbt29me//Vq1dDBRXcw9z1zlGVmSQhCe6h7lAKSvzwww+wsrKCSqWCUqnM1vFu376J69fZi5KIiIiIiIiIiIgoJ+TpYJWLiwsAYOnSpShfvry43NraGitWrICNjQ28vb3x9u1bJCQkAAAKFiyo81jq5eo5qNT/L1SoUJa2J6LU3ksuLn8bdYyaNWtCJpEhID7AqOMExAdAJpGhePHiGDNmjJi+7PaQ2r59c7Z7ZxERERERERERERGRfnk2WPXu3Tu8efMGpUuXRv369dOtt7GxgaOjIwRBwNWrVyGVpp6qRCLReTz1C2z1/w3dnohMQy6XAwDilcYFgo3dn4iIiIiIiIiIiIg+jjwbrFIP61euXDm926h7W4WGhsLKygoAkJiYqHPbpKTU4cbU22V1e309r4jyIwsLC4wcOcaoYygUCgCAlczKqOOo94+Pj8eWLVvE9OkLQGdm1KhxsLCwMCpNRERERERERERERJReng1WqXs0ZfTiWT0/jaWlJUqWLAngvzmp0ko7R5Wh2xNRKgeHxqhbN31vx6x68OABlIIStaxqGZWOWla1oBSUePDgARISEiCVSiGTybJ1rLp168PBoZFR6SEiIiIiIiIiIiIi3fJssEodTHr16pXebdTr7OzsUK1aNQDA48eP020nCAKePn0KmUyGqlWrAkCG2wPAkydPAADVq1fP5hkQfZokEglGjRqX7R5MU6ZMgRRS9LTriQKSAtk6RgFJAfQs3hNSSPHdd98BQLZ7RRl7PkRERERERERERESUsTwbrCpbtizKlSuH4OBg3Lx5M936mJgYXLhwAVKpFA0bNkSrVq0AAKdPn0637Y0bNxAeHg4HBwdYW1sDABo1aoSCBQvi0qVLiI/XnvsmLi4Oly5dgpWVFRwcHHLg7IjytooVP0enTl2yvb9CoYCN3AZORZ2ytX/Hoh1hLbMWhxSUy+XiPHSG+vrrrqhY8fNs7UtEREREREREREREmcuzwSoAGDFiBABg9uzZeP36tbg8Li4OM2bMQGxsLDp27IiSJUuicePGqFatGi5cuID9+/eL24aHh2PBggUAgFGjRonLrays0LNnT0RFRWHBggXiS2+FQoHffvsN0dHRGDBggBjcIiJtAwcOReHChbO1b7t27aBQKTCx7ETYye0M2re4vDgmlJ0AhUqBZs2aoUSJkqha9YtspcPKygoDBgzJ1r5ERERERERERERElDUSQT35Ux6kUqkwbdo0eHt7o0CBAmjUqBEKFSqEa9euISIiAl9++SW2bduGokWLAgDu3LmDESNGID4+HnXr1kXJkiVx5coVREVFoX///li4cKHW8SMjIzFw4EA8e/YMFSpUwJdffon79+/j1atX+PLLL7Fr165sv4zPTHKyAlFRCTlybKKP5ehRd2zfvhkA0KRJM/j7X8ryvqdPn4bMQoaniU8x6dEkhCnCMt2nuLw41ldbj0oFK0GZrESLFi0wZ848ODq2Re/ehvf0Gj16HLp162nwfkRkfiVK2AAAQkJizJwSIsqLmIcQkTGYhxCRMZiHEJExckseok6HIfJ0sApInW/q8OHD2L9/PwIDAwEAlSpVwjfffIOhQ4fC0tJSa/vHjx9jzZo18Pf3R3JyMj7//HMMHDgQ/fr1g0wmS3f8yMhIrFu3DqdOnUJYWBjKlCkDJycnjB8/HjY2hl/wrGKwij4FKSkpcHc/CAD45pueOHRoP86dO4u4uFgkJCQgs+zn7NmzkMgkCE0JhfNbZ/hE+CBJSEq3XQFJAXQs2hETyk6AnYUdoASaNm2Kr76qh6VL/4REIoGPjzc2blyr83MsLS1RtWo1SKVSVK9eE48eBeLLL2ujT58BkMvlxl8IIvrockvhjIjyJuYhRGQM5iFEZAzmIURkjNySh+TLYNWnisEqys8GD+6DxMQEFCpUCJ6enpDKpZBL5YhVxsI91B0B8QGIV8bDSmaFWla10LN4T1jLrKEUlFAkK9CqVSuoVCps2LAVlStXSXfcggULYc+eQ2Y8QyLKabmlcEZEeRPzECIyBvMQIjIG8xAiMkZuyUOyE6xilwEiynUGDBgCT8/DGDhwIKKiUntS2doWRGF5YQwqOQgyyX+9IJWCUvxbCim+//57qFQqyGQyrUCV+rhHjrihe/feH+9kiIiIiIiIiIiIiChDDFYRUa7To0dvjBkzAsB/rQAiIxMRExONiIj3qFmzJuRyOSwtLbUCVydPnsSNGzcAABYWFjqP26MHA1VEREREREREREREuQmDVUSUZ9jYfIYhQ/oiJSUFpUqVwsGDB1GoUCEAQEJCAv766y8AqYEqiURizqQSERERERERERERURZJzZ0AIiJDyGQySCQSBAcHw8XFRVy+fft2BAcHQyKRQCaT6T8AEREREREREREREeUqDFYRUZ4ikUjEIf527tyJ169f4/Xr19i5cycA9qoiIiIiIiIiIiIiyms4DCAR5TkymQwymQzJyclYtWoVBEFASkoKPivngJTw++ZOHhEREREREREREREZgMEqIsqTLCwsoFQq4evrKy5LDgtgryoiIiIiIiIiIiKiPIbDABJRniSRSCCX/xdvl8vlDFQRERERERERERER5UEMVhFRnqUOUKUNXBERERERERERERFR3sG3u0SUp7i5eWn9fe3aFQBAw4aNzZEcIiIiIiIiIiIiIjISg1VElKcxSEVERERERERERESUt3EYQCIiIiIiIiIiIiIiIjIbBquIiIiIiOiTd+3aFXH4YCIiIiIiIspdOAwgERERERF90lJSUrBx4zoAgLPzVlhYWJg5RURERERERKSJwSoiIiIiIvok9e7dBUBqsEqhUIjL0gar3Ny8PnraiIiIiIiI6D8cBpCIiIiIiD5ZgiCIgSoAUCgUEATBjCkiIiIiIiKitBisIiIiIiKiT1ZKSgoAoHXr1nB0dNRaRkRERERERLkDhwEkIiIiIqJPklKphFKphKWlJaZOnQoAuHz5MpKTk6FUKiGTycycQiIiIiIiIgLYs4qIiIiIiD5BSqVS7EE1bNgwlC9fHuXLl8fQoUMBpPau4nCAREREREREuQODVURERERE9Mk5ccILgiCgVKlSGDlypLh81KhRKFWqFARBgFKpNF8CiYiIiIiISMRgFRERERERfVJiYqLxzz9/AwCmTJmCQoUKiesKFSqEH3/8EQB7VxEREREREeUWnLOKiIiIiIg+KaVLF8Xx48ehUCi0AlVqTk5OOHjwIG7cuCEOFUhERERERETmw55VRERERESUJ3l4uOHbb4fCw8MNtrYFUby4NYoVt4KVlRUsLS1hZWUFFVTp9pNIJJgxYwakUimUSiWePXuqtd7V1RU9e/aEh4fbxzoVIiIiIiKifE0i5LNxL549e4a1a9fi+vXriIyMRMWKFTFgwAAMHjwYUmn62F2HDh3w6tUrvccLCAiAXG76DmrJyQpERSWY/LhEeUWJEjYAgJCQGDOnhIjyIuYhRJ+GiIgwfP/9d4iPj4dMJgeQfp6p06dPQyqXQi6VI0YRA/cwdwTEByBeGQ8rmRVqWdVCT7uesJHbQCEoIJf8V3Zfvnw59u/fj6++qoelS/+ERCIBAPTu3UVvmiQSCYoWLYY2bdpBIpEhKOgBatashR49esPT0wMA0LNnX1hYWJj+ghBRnsByCBEZg3kIERkjt+Qh6nQYIl8NA/jw4UMMGTIEsbGxaNCgAerUqQN/f38sXLgQt27dwooVK7S2j4mJwevXr1G8eHE0a9ZM5zF1BbiIiIiIiMh406f/gPj4eACAUqlIt/7s2bOADAhLCcOGtxtwMuIkkoQkrW1ORpyE81tnOBV1wsSyE1HcojhkEhkA4LvvvsOJEydw584tnD//Lxwd22aaJkEQEB4eBje3A+Kyu3dv4/nzZ7hy5RKA1HmxunXrmb2TJiIiIiIiyofyTbBKEATMnDkTsbGxWL58OXr06AEACA8Px8iRI3H06FE4OTmhU6dO4j4PHjyAIAho27YtFi1aZK6kExERERHlOzduXEVkZITe9adPnwZkwLPEZ5j0aBLCFGF6t00SkuAZ7onL0Zexrto6VClYBTKJDEWKFMGkSZOwePFibNnijMaNm2Lu3FnZSq86UAUA+/bthqNjW3z2WZFsHYuIiIiIiCi/yTfdgi5cuIDAwEA0btxYDFQBQLFixTB//nwAwM6dO7X2uX//PgCgVq1aHy+hRERERESEZcsybiwmlUsRlhKWaaBKU6giFJMeTUJoSiiUQupwgj169ECNGjUQGhqC/fv34smTx0anPS4uDvv27Tb6OERERERERPnFJxGs8vDwwODBg9GgQQN89dVX+Oabb7Bp0yYkJiaK25w/fx5A6hxUaTk4OMDOzg7Xr19HbGysuPzBgwcAGKwiIiIiIvqYduzYhpSUFL3rz5w5A7lUjg1vN2Q5UKUWpgiD81tncShAmUyGGTNmAAD27t0JlUqV/YRrOHHCCy9fvjDJsYiIiIiIiD51eTpYpVKpMH36dMycORP37t1D/fr10apVK3z48AF//vknBg8ejOjoaADA48epLSTt7e11Hqty5cpQqVR48uSJuOz+/fuQyWR49uwZhgwZgoYNG6JRo0YYP3487ty5k/MnSERERESUzygUCri7H8xwG7lcjhhFDE5GnMzWZ/hE+CBW+V8jtbp166JLly4AkGGQzBAqlQouLlsgCIJJjkdERERERPQpy9PBql27dsHT0xOVKlWCl5cXtm7divXr1+P06dNo06YNAgICxCH+Pnz4AAAoUaKEzmOpl4eGhgIAkpOT8fTpUyiVSsycORNJSUlo0qQJihYtirNnz2Lw4ME4fvz4RzhLIiIiIqL8Y/78ORmuX716NVRQwT3MHUlCUrY+I0lIgnuouzgUIAD88MMPsLKygkqlglKpzGDvrLt16wauX79qkmMRERERERF9yvJ0sMrFxQUAsHTpUpQvX15cbm1tjRUrVsDGxgbe3t54+/YtEhISAAAFCxbUeSz18vj4eABAYGAgFAoFChcuDBcXFxw8eBDr16+Hj48P5syZg5SUFMyZMwchISE5eIZERERERPlHREQYHjwIyHCbmjVrQiaRISA+4+0yExAfIA4FCADFixfHmDFjAKT2rjJVjygXly0m661FRERERET0qcqzwap3797hzZs3KF26NOrXr59uvY2NDRwdHSEIAq5evQqpNPVUJRKJzuOpK6Pq/9epUwd+fn44evQomjVrprXtyJEj0aFDB8THx+Pw4cOmPC0iIiIiIsqAXC4HAMQr4406jrH7ExERERERkenk2WCVeli/cuXK6d1G3dsqNDQUVlZWAIDExESd2yYlpQ4hot4OSB0aUN/x27ZtCwC4d++egSknIiIiIiJdiha1Q82atTLcRqFQAACsZFYZbpeZtPuHhIRgy5YtAAALCwu9jdwMNXLkWFhYWJjkWERERERERJ+qPBusUveAyqgSqR5r3tLSEiVLlgTw35xUaamH89M3p1Va6u30Bb+IiIiIiMhwCxYsyXD9gwcPoBSUqGWVcVArM7WsamnNWbV27VokJCRAKpVCJpNlsGfW1avXAA4OjUxyLCIiIiIiok9Zng1WqYNPr1690ruNep2dnR2qVasGAHj8+HG67QRBwNOnTyGTyVC1alUAgJeXF6ZPn46jR4/qPPbr168BAKVLl87+SRARERERkRa5XI5evfrpXT9lyhRIIUVPu54oICmQrc8oICmAnsV7inNW3b59G15eXgBgsl5QUqkUI0eONVkPLSIiIiIiok9Zng1WlS1bFuXKlUNwcDBu3ryZbn1MTAwuXLgAqVSKhg0bolWrVgCA06dPp9v2xo0bCA8Ph4ODA6ytrQEAYWFh8PT0xN69e9NtLwgCjhw5AgBo2bKlKU+LiIiIiCjfGzZsVIZBI4VCARu5DZyKOmXr+B2LdoS1LLXcr1Qq8ccffwAABg0aJs51a6xOnbqgYsXPTXIsIiIiIiKiT12eDVYBwIgRIwAAs2fPFns6AUBcXBxmzJiB2NhYdOzYESVLlkTjxo1RrVo1XLhwAfv37xe3DQ8Px4IFCwAAo0aNEpd37doV1tbWuH79OlxcXMTlgiBg/fr1uHXrFuzt7dGuXbscPksiIiIiovxn1qxf9K5r164dFCoFJpadCDu5nUHHLS4vjgllJ4hDAHp4eODhw4coUaIk+vcfhKpVvzAq3QBQuHBhDBgwxOjjEBERERER5RcSQT35Ux6kUqkwbdo0eHt7o0CBAmjUqBEKFSqEa9euISIiAl9++SW2bduGokWLAgDu3LmDESNGID4+HnXr1kXJkiVx5coVREVFoX///li4cKHW8U+cOIHp06cjJSUF1apVQ5UqVRAYGIjnz5+jRIkS2LVrFypVqpQj55acrEBUVEKOHJsoLyhRwgYAEBISY+aUEFFexDyE6NMwevQQREZG6Fx3+vRpyCxkeJr4FJMeTUKYIizT4xWXF8f6autRqWAlyCVyREVFoXfv3oiKisKcOfPg6NgWANC7dxeD09q4cTNcuXLp/9M9Dt269TT4GET0aWA5hIiMwTyEiIyRW/IQdToMkaeDVUBqT6fDhw9j//79CAwMBABUqlQJ33zzDYYOHQpLS0ut7R8/fow1a9bA398fycnJ+PzzzzFw4ED069dP50TK9+7dw6ZNm3D16lXExsaiZMmSaNeuHSZOnIhixYrl2HkxWEX5XW7JWIkob2IeQvRpiIgIw/fff4f4+HjIZHIAApRKpbj+7NmzkMgkCE0JhfNbZ/hE+CBJSEp3nAKSAuhYtCMmlJ0AOws7yCVyAMDy5cuxf/9+fPVVPSxd+qc4v1RGwSqJRIJixezQpk07SCQyBAbex5df1kb37r3h6ekOAOjVqx/kcrnpLgQR5SkshxCRMZiHEJExckseki+DVZ8qBqsov8stGSsR5U3MQ4jyh8GD++DYMU9I5VLIpXLEKmPhHuqOgPgAxCvjYSWzQi2rWuhZvCesZdZQCkrIJKkN1B49eoQhQ4ZApVJhw4atqFy5injcIUP6ICEhAQULFsKePYfMdXpElEexHEJExmAeQkTGyC15SHaCVWzuR0REREREedKAAUPQt28/dO/eGyNGDEZheWEMKjlIDEgB0ApQqf8vCAL++OMPqFQqyGQyrUAVAIwePRqurq7o1q3XxzsZIiIiIiKifIzBKiIiIiIiypN69OiNHj16AwAiIxPF5YULyyCXy6FQKPDgwQM4ODho7Xfy5EncuHEDAGBhYZHuuAMHDsTAgQPN3hqRiIiIiIgov5CaOwFERERERESmFBenxN69B+Ho6Ih58+YhIeG/4bUTEhLw119/AUgNVKnnqSIiIiIiIiLzYbCKiIiIiIg+OZ06dYFEIkFwcDBcXFzE5du3b0dwcDAkEglkMpn+AxAREREREdFHw2AVERERERF9cmQymTjE386dO/H69Wu8fv0aO3fuBMBeVURERERERLkJ56wiIiIiIqJPkkwmg0wmQ3JyMlatWgVBEJCSkoJWlWxxLTjJ3MkjIiIiIiKi/8dgFRERERERfbIsLCygVCrh6+srLrv6PpG9qoiIiIiIiHIRDgNIRERERESfLIlEArn8vzZ6crmcgSoiIiIiIqJchsEqIiIiIiL6pKkDVGkDV0RERERERJQ7sKZGRERERESfJDc3L/Hf165dAQA0bNjYXMkhIiIiIiIiPRisIiIiIiKiTx6DVERERERERLkXhwEkIiIiIiIiIiIiIiIis2GwivK9a9euiMPCEBERERERERERERHRx8VhAClfS0lJwcaN6wAAzs5bYWFhYeYUERERERERERERERHlLwxWUb7Tu3cX8d8pKSlQKBTics1gleaE3ERERERERERERERElDM4DCDlW4IgiIEqAFAoFBAEwYwpIiIiIiIiIiIiIiLKfxisonwrJSUFANC6dWs4OjpqLSMiIiIiIiIiIiIioo+DwwBSvqRUKqFUKmFpaYmpU6cCAC5fvozk5GQolUrIZDIzp5CIiIiIiIiIiIiIKH9gzyrKdwRBEHtQDRs2DOXLl0f58uUxdOhQAKm9qzgcIBERERERERERERHRx8FgFeU7SqUSgiCgVKlSGDlypLh81KhRKFWqFARBgFKpNF8CiYiIiIiIiIiIiIjyEQarKF+JiYkWe1VNmTIFhQoVEtcVKlQIP/74I4DU3lUxMTFmSSMRERERERERERERUX7COasoX9m50wUA4ODggA4dOqRb7+TkhJYtW0Iul0OhUCAujj2siIiIiIiIiIiIiIhyEntW0SfHw8MN3347FB4eblrLnz17gmPHPCCVSjFjxgxIJJJ0+6qggpWVFSwtLWFlZYVixa1QvLg1bG0LZnhsIiIiIiIiIiIiIiLKHokgCIK5E0HpJScrEBWVYO5k5DopKSlwdz+IhIR4+Ph4IykpKcP5pdzcvAAAgiBg1qypuHv3NgYMGIAZM2aI2ygEBeQSOWIUMXAPc0dAfADilfGwklmhllUt9LTrCRu5DRQqBVQKFdq3b6/zs4oVs0OrVq3x5MljAMCXX9ZGnz4DYGFhYcIrkH+UKGEDAAgJ4XCMRGQ45iFEZAzmIURkDOYhRGQM5iFEZIzckoeo02EIDgMI4OnTp+jRowcmTJiAiRMnZrjt2rVrsW7dOvj6+qJ06dIfKYWkdvz4Mezdu9Pg/c6dO4u7d2/D1tYW3333nbhcKSgRlhKGDW834GTESSQJSVr7nYw4Cee3znAq6oSJZSeiuEVxnD17Fm3btk33GeHhYVo9ru7duwNra2t069bT4PQSEREREREREREREeUX+X4YwPDwcEyePBnJycmZbnvq1Cls3LjxI6SKdImOjsL+/bsN2mfGjB+QmJiAv/9O/d4mTZqEzz77DEBqoOpp4lMMezgMnuGe6QJVaklCEjzDPTH84XA8TXwKiUyC06dPZ+nzXV13ITo6yqA0ExERERERERERERHlJ/k6WBUUFIRBgwbhyZMnmW67e/duTJkyBQqF4iOkjHRxdd2FuLg4g/Z58uQx9u3bg9DQENSsWRPdu3cHkDr0X2hKKCY9moQwRViWjhWqSN0+NCUUUnnWbp34+Hjs22dYgI2IiIiIiIiIiIiIKD/Jl8GqhIQErFu3Dv3798fz589Rvnx5vds+efIE48aNw2+//QZra2sULlz4I6aU1F68eI4TJ7wM3k+lUsHVdRcAYMaMGZDJZAAAuUSODW83ZDlQpRamCIPzW2fIpXKcOXMmS/scP34ML1++MCzhRERERERERERERET5xCcVrPLw8MDgwYPRoEEDfPXVV/jmm2+wadMmJCYmam3n7e2NtWvXwtraGuvWrUPPnj31HvPXX3+Fr68vWrRoATc3N9ja2ubsSVA6giDAxWULBEEweN+UlBQAQNeuXfHVV1+Jy2MUMTgZcTJb6fGJ8EGsMhZyedamfBMEAdu3b85W+omIiIiIiIiIiIiIPnWfRLBKpVJh+vTpmDlzJu7du4f69eujVatW+PDhA/78808MHjwY0dHR4va2trb4/vvvceLECTg5OWV47Nq1a8PZ2Rnbtm1D2bJlc/pUSIfr16/g9u2bBu+nVCqhUqlgZWWF77///r/lghLuYe5656jKTJKQBPdQd6igwurVq7O0z+3bN3H9+tVsfR4RERERERERERER0acsa11Dcrldu3bB09MTlSpVwtatW8Vh/WJjYzF9+nT8+++/mD9/PlatWgUAaNeuHdq1a5elY8+aNSvH0k2ZS0lJgYvL3wbvJwiC2Ktq7NixKF68uLhOJpEhID7AqHQFxAdAJpGhZs2aWd5n+/bNqFu3PiwsLIz6bCIiIiIiIiIiIiKiT8kn0bPKxcUFALB06VKt+aesra2xYsUK2NjYwNvbG2/fvjVTCim3iVfGm2T/rA4FSEREREREREREREREuuX5YNW7d+/w5s0blC5dGvXr10+33sbGBo6OjhAEAVevchi2vMbCwgIjR44xeD+JRCL2YNqyZQtCQ0O11lvJrIxKl3p/hUKR5X1GjRrHXlVERERERERERERERGnk+WDVhw8fAADlypXTu426t1XagAXlDQ4OjVG3bvpAZGZkMhmkUini4+Oxdu1acblSUKKWVS2j0lTLqhaUghIPHjzI0vZ169aHg0Mjoz6TiIiIiIiIiIiIiOhTlOeDVYIgAEjtSaOPUqkEAFhaWn6UNJFpSSQSjBo1LsPvWB91T6Zjx47h9u3bAFLnrOpp1xMFJAWylZ4CkgLoWbwnpJBiypQpmW5vTPqJiIiIiIiIiIiIiD51eT5YVbJkSQDAq1ev9G6jXmdnZ/dR0kSmV7Hi5+jUqYvB+0mlUgwcOBQAsGLFCjFwaSO3gVNRp2ylpWPRjrCWWWd5CMCvv+6KihU/z9ZnERERERERERERERF96vJ8sKps2bIoV64cgoODcfPmzXTrY2JicOHCBUilUjRs2NAMKSRTGThwKAoXLmzQPlWrfoEBAwajePESePDgAY4cOQIAUAgKTCw7EXZywwKYxeXFMaHsBChUCrRr1y7T7a2srDBgwBCDPoOIiIiIiIiIiIiIKD/J88EqABgxYgQAYPbs2Xj9+rW4PC4uDjNmzEBsbCw6duwo9sKivOmzz4qgf3/DAj9//LEGBQsWwpgx4wEA69evR3R0NOQSOYpbFMf6auuzHLAqLv//7S3soExRZmmfgQOH4rPPihiUZiIiIiIiIiIiIiKi/ERu7gSYwrBhw3Dz5k14e3ujS5cuaNSoEQoVKoRr164hIiICX375JX799VdzJ5NM4OuvuyIxMQEJCfHw8fFGUlKSOLRfRhwd2+LYsSO4e/c2Nm3ahBkzZkAmkaFKwSrYWWMnnN86wyfCB0lCUrp9C0gKoGPRjphQdgLsLOwAJdChQ4d02xUrZofWrdvi0aMgAECtWnXw9dfdjD9pIiIiIiIiIiIiIqJP2CcRrJJKpVi1ahUcHR2xf/9+3LhxAwBQqVIljBs3DkOHDoWlpaWZU0mmYGFhgX79BgEAhg//Vuc2gwf3QWJiAgoWLCQuk0gkmDDhe0yePA4HDhxAr1698MUXX0AmkaG4RXH8WulX/FThJ7iHuiMgPgDxynhYyaxQy6oWehbvmTpHlUoBZbISHTp0QMGChbBnz6GPcs5ERERERERERERERJ8yiSAIgrkTQeklJysQFZVg7mTkSR4ebjhyxA3du/dGjx69tdZt2LAGR48ehoODAzZu3AiJRKK1XikoIZPItP6WQgqFQoHIyMQMj02mVaKEDQAgJCTGzCkhoryIeQgRGYN5CBEZg3kIERmDeQgRGSO35CHqdBiCwapcisGqnBETE43+/XsAAJYsWQInJyet9devX0fNmjUhl8uhUCgQF5e1uanI9HJLxkpEeRPzECIyBvMQIjIG8xAiMgbzECIyRm7JQ7ITrJLmQDqIci0bm89gYWEBAFi9ejUSEv4LCCYkJGDevHlwdHRE69atGagiIiIiIiIiIiIiIvoIGKyifEcmk0EikSA4OBguLi7i8u3btyM4OBgSiQQymUz/AYiIiIiIiIiIiIiIyGQYrKJ8RyKRiL2rdu7cidevX+P169fYuXMnAMDCwiLdXFZERERERERERERERJQz5OZOAJE5yGQyyGQyJCcnY9WqVRAEASkpKeJyIiIiIiIiIiIiIiL6OBisonzLwsICSqUSvr6+WsuIiIiIiIiIiIiIiOjj4TCAlG9JJBLI5f/Fa+VyOYf/IyIiIiIiIiIiIiL6yBisonxNHaBKG7giIiIiIiIiIiIiIqKPg2/nKd9xc/PS+vvatSsAgIYNG5sjOURERERERERERERE+RqDVZTvMUhFRERERERERERERGQ+HAaQiIiIiIiIiIiIiIiIzIbBKiIiIiIiIiIiIiIiIjIbBquIiIiIiIiIKMuuXbsizv1LRERERGQKnLOKiIiIiIiIiPTq3buL+G9BEJCUlAQAKFCgACQSSbrt3dy8PlraiIiIiOjTwJ5VRERERERERJQlCoUCgiBAEAQoFApzJ4eIiIiIPhEMVhERERERERFRptIGqNSBKyIiIiIiYzFYRURERERERESZSklJAQC0bt0ajo6OAICvbGzwR4MG5kwWEREREX0COGcVERERERE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+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 285,
+       "width": 853
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_yhat_vs_y(\n",
+    "    df_h: pd.DataFrame, yhat: str = \"yhat\", y: str = \"y\", mdlnm: str = \"mdla\"\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience plot forecast yhat with overplotted y from holdout set\"\"\"\n",
+    "    g = sns.catplot(x=yhat, y=\"oid\", data=df_h.reset_index(), **KWS_BOX, height=4, aspect=3)\n",
+    "    _ = g.map(sns.scatterplot, y, \"oid\", **KWS_SCTR, zorder=100)\n",
+    "    _ = g.fig.suptitle(\n",
+    "        f\"Out-of-sample: boxplots of posterior `{yhat}` with overplotted actual `{y}` values\"\n",
+    "        + f\" per observation `oid` (green dots) - `{mdlnm}`\"\n",
+    "    )\n",
+    "    _ = g.tight_layout()\n",
+    "    return g.fig\n",
+    "\n",
+    "\n",
+    "_ = plot_yhat_vs_y(dfraw_h_y, mdlnm=\"mdl0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ The predictions `yhat` look very close to the true value `y`, usually well within the $HDI_{94}$ and $HDI_{50}$\n",
+    "+ As we would expect, the distributions of `yhat` are useful too: quantifing the uncertainty in prediction and letting us\n",
+    "  make better decisions accordingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. `ModelA`: Auto-impute Missing Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we progress to handling missing values!\n",
+    "\n",
+    "`ModelA` is an extension of `Model0` with a simple linear submodel with a hierarchical prior on the data for \n",
+    "features $k$ that have missing values:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\sigma_{\\beta} &\\sim \\text{InverseGamma}(11, 10)  \\\\\n",
+    "\\beta_{j} &\\sim \\text{Normal}(0, \\sigma_{\\beta}, \\text{shape}=j)  \\\\\n",
+    "\\beta_{k} &\\sim \\text{Normal}(0, \\sigma_{\\beta}, \\text{shape}=k)  \\\\\n",
+    "\\\\\n",
+    "\\mu_{k} &\\sim \\text{Normal}(0, 1, \\text{shape}=k)  \\\\\n",
+    "\\mathbb{x}_{ik} &\\sim \\text{Normal}(\\mu_{k}, 1, \\text{shape}=(i,k))  \\\\\n",
+    "\\\\\n",
+    "\\epsilon &\\sim \\text{InverseGamma}(11, 10)  \\\\\n",
+    "\\hat{y_{i}} &\\sim \\text{Normal}(\\mu=\\beta_{j}^{T}\\mathbb{x}_{ij} + \\beta_{k}^{T}\\mathbb{x}_{ik}, \\sigma=\\epsilon)  \\\\\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where:\n",
+    "+ Observations $i$ contain numeric features $j$ that have complete values, and numeric features $k$ that contain \n",
+    "  missing values\n",
+    "+ For the purposes of this example, we assume that in future, features $j$ will always be complete, and missing values \n",
+    "  can occur in $k$, and design the model accordingly\n",
+    "+ This is a big assumption, because missing values could theoretically occur in any feature, but we extend the example \n",
+    "  to assume we always require that features $j$ are complete\n",
+    "+ We treat data $\\mathbb{x}_{ik}$ as a random variable, and \"observe\" the non-missing values. \n",
+    "+ We will assume the missing data values to have a Normal distribution with mean $\\mu_{k}$ - this is reasonable because\n",
+    "  we zscored the `dfx` data and can expect a degree of centrality around 0. Of course, the actual distributions of data\n",
+    "  in $\\mathbb{x}_{ik}$ could be highly skewed, so a Normal is not necessarily the best choice, but a good place to start\n",
+    "+ Our target is $\\hat{y_{i}}$, here of `y` with linear sub-models $\\beta_{j}^{T}\\mathbb{x}_{ij}$ and \n",
+    "  $\\beta_{k}^{T}\\mathbb{x}_{ik}$ to regress onto those features\n",
+    "\n",
+    "\n",
+    "**IMPLEMENTATION NOTE**\n",
+    "\n",
+    "There are a few ways to handle missing values in `pymc`. Here in `ModelA` we make use of `pymc` \"auto-imputation\",\n",
+    "where we instruct the model to \"observe\" a slice of the dataset `dfx_train[FTS_XK]` for the features that contain \n",
+    "missing values , and the `pymc` build processes will very kindly handle the missing values and provide auto-imputation \n",
+    "at element level.\n",
+    "\n",
+    "This is convenient and clean in the model specification, but does require further manipulation of the model and idata\n",
+    "to create out-of-sample predictions, see $\\S 2.5$ for detail\n",
+    "\n",
+    "In particular:\n",
+    "+ The auto-imputation routine will create flattened `xk_observed` and `xk_unobserved` RVs for us, based on a (single)\n",
+    "  specified distribution of `xk`, and a final `Deterministic` for `xk` with the correct dimensions\n",
+    "+ **However, an important limitation** is that currently, `pm.Data` **cannot** contain `NaNs` (nor a `masked_array`) so\n",
+    "  we can't use the usual workflow `mdl.set_data()` to replace the in-sample dataset with an out-of-sample dataset\n",
+    "  to make predictions!\n",
+    "+ For example, neither of these constructs is possible:\n",
+    "\n",
+    "  1. Cannot insert `NaNs` into `pm.Data`\n",
+    "      ```python\n",
+    "      xk_data = pm.Data('xk_data', dfx_train[FTS_XK].values, dims=('oid', 'xk_nm'))\n",
+    "      xk_ma = np.ma.masked_array(xk_data, mask=np.isnan(xk_data.values))\n",
+    "      ```\n",
+    "  2. Cannot insert `masked_array` into `pm.Data`\n",
+    "      ```python\n",
+    "      xk_ma = pm.Data('xk_ma', np.ma.masked_array(dfx_train[FTS_XK].values, mask=np.isnan(dfx_train[FTS_XK].values)))\n",
+    "      ```\n",
+    "+ Also see further discussion in [pymc issue #6626](https://github.com/pymc-devs/pymc/issues/6626), and \n",
+    "  [proposed new functionality #7204](https://github.com/pymc-devs/pymc/issues/7204)\n",
+    "+ Finally, note that some earlier examples of auto-imputation involve creating a `np.ma.masked_array` and passing that\n",
+    "  into the observed RV. As at Dec 2024 this appears to no longer be necessary, and we can directly pass in the dataframe\n",
+    "  \n",
+    "  For example, this:\n",
+    "    ```python\n",
+    "    xk_ma = np.ma.masked_array(dfx_train[FTS_XK].values, mask=np.isnan(dfx_train[FTS_XK].values))\n",
+    "    xk = pm.Normal(\"xk\", mu=xk_mu, sigma=1.0, observed=xk_ma, dims=(\"oid\", \"xk_nm\"))\n",
+    "    ```\n",
+    "\n",
+    "  can now be simply stated as:\n",
+    "    ```python\n",
+    "    xk = pm.Normal(\"xk\", mu=xk_mu, sigma=1.0, observed=dfx_train[FTS_XK].values, dims=(\"oid\", \"xk_nm\"))\n",
+    "    ```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Build Model Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jon/miniforge/envs/oreum_survival/lib/python3.11/site-packages/pymc/model/core.py:1366: ImputationWarning: Data in xk contains missing values and will be automatically imputed from the sampling distribution.\n",
+      "  warnings.warn(impute_message, ImputationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ft_y = \"y\"\n",
+    "FTS_XJ = [\"intercept\", \"a\", \"b\"]\n",
+    "FTS_XK = [\"c\", \"d\"]\n",
+    "COORDS = dict(\n",
+    "    xj_nm=FTS_XJ,  # names of the features j\n",
+    "    xk_nm=FTS_XK,  # names of the features k\n",
+    "    oid=dfx_train.index.values,  # these are the observation_ids\n",
+    ")\n",
+    "\n",
+    "with pm.Model(coords=COORDS) as mdla:\n",
+    "    # 0. create (Mutable)Data containers for obs (Y, X)\n",
+    "    y = pm.Data(\"y\", dfx_train[ft_y].values, dims=\"oid\")\n",
+    "    xj = pm.Data(\"xj\", dfx_train[FTS_XJ].values, dims=(\"oid\", \"xj_nm\"))\n",
+    "\n",
+    "    # 1. create auto-imputing likelihood for missing data values\n",
+    "    # NOTE: there's no way to put a nan-containing array (nor a np.masked_array)\n",
+    "    # into a pm.Data, so dfx_train[FTS_XK].values has to go in directly\n",
+    "    xk_mu = pm.Normal(\"xk_mu\", mu=0.0, sigma=1, dims=\"xk_nm\")\n",
+    "    xk = pm.Normal(\n",
+    "        \"xk\", mu=xk_mu, sigma=1.0, observed=dfx_train[FTS_XK].values, dims=(\"oid\", \"xk_nm\")\n",
+    "    )\n",
+    "\n",
+    "    # 2. define priors for contiguous and auto-imputed data\n",
+    "    b_s = pm.Gamma(\"beta_sigma\", alpha=10, beta=10)  # E ~ 1\n",
+    "    bj = pm.Normal(\"beta_j\", mu=0, sigma=b_s, dims=\"xj_nm\")\n",
+    "    bk = pm.Normal(\"beta_k\", mu=0, sigma=b_s, dims=\"xk_nm\")\n",
+    "\n",
+    "    # 4. define evidence\n",
+    "    epsilon = pm.Gamma(\"epsilon\", alpha=50, beta=50)  # encourage E ~ 1\n",
+    "    lm = pt.dot(xj, bj.T) + pt.dot(xk, bk.T)\n",
+    "    _ = pm.Normal(\"yhat\", mu=lm, sigma=epsilon, observed=y, dims=\"oid\")\n",
+    "\n",
+    "RVS_PPC = [\"yhat\"]\n",
+    "RVS_PRIOR = [\"epsilon\", \"beta_sigma\", \"beta_j\", \"beta_k\"]\n",
+    "RVS_K = [\"xk_mu\"]\n",
+    "RVS_XK = [\"xk\"]\n",
+    "RVS_XK_UNOBS = [\"xk_unobserved\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Verify the built model structure matches our intent, and validate the parameterization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
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+       "<!-- yhat&#45;&gt;y -->\n",
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+       "<text text-anchor=\"middle\" x=\"231\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- xk_observed&#45;&gt;xk -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>xk_observed&#45;&gt;xk</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M104.3,-378.44C116.54,-365.21 131.78,-351 148,-341.23 158.35,-334.98 163.13,-338.52 174,-333.23 177.46,-331.54 180.95,-329.65 184.41,-327.65\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"186.03,-330.76 192.7,-322.53 182.35,-324.8 186.03,-330.76\"/>\n",
+       "</g>\n",
+       "<!-- xk_unobserved&#45;&gt;xk -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>xk_unobserved&#45;&gt;xk</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M230.33,-373.49C230.44,-360.46 230.56,-346 230.67,-333\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"234.17,-333.1 230.75,-323.08 227.17,-333.05 234.17,-333.1\"/>\n",
+       "</g>\n",
+       "<!-- xk&#45;&gt;yhat -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>xk&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M254.48,-263.51C266.59,-249.04 281.57,-231.14 294.84,-215.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"297.3,-217.8 301.03,-207.88 291.93,-213.3 297.3,-217.8\"/>\n",
+       "</g>\n",
+       "<!-- beta_sigma -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>beta_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"347\" cy=\"-661.52\" rx=\"55.33\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-672.97\" font-family=\"Times,serif\" font-size=\"14.00\">beta_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-656.47\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-639.97\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- beta_sigma&#45;&gt;beta_k -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>beta_sigma&#45;&gt;beta_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M329.5,-622.44C325.02,-612.7 320.16,-602.12 315.51,-592\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"318.75,-590.67 311.39,-583.05 312.39,-593.6 318.75,-590.67\"/>\n",
+       "</g>\n",
+       "<!-- beta_j -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>beta_j</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"400\" cy=\"-544.2\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-555.65\" font-family=\"Times,serif\" font-size=\"14.00\">beta_j</text>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-539.15\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-522.65\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_sigma&#45;&gt;beta_j -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>beta_sigma&#45;&gt;beta_j</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M364.5,-622.44C368.98,-612.7 373.84,-602.12 378.49,-592\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"381.61,-593.6 382.61,-583.05 375.25,-590.67 381.61,-593.6\"/>\n",
+       "</g>\n",
+       "<!-- epsilon -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>epsilon</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"437\" cy=\"-292.57\" rx=\"42.6\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">epsilon</text>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"437\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- epsilon&#45;&gt;yhat -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>epsilon&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M408.93,-261.72C394.96,-246.87 377.94,-228.77 363.12,-213.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"366.03,-211 356.63,-206.11 360.94,-215.79 366.03,-211\"/>\n",
+       "</g>\n",
+       "<!-- beta_j&#45;&gt;yhat -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>beta_j&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M392.32,-503.81C380.78,-444.64 358.27,-329.3 339,-231.32 338.66,-229.57 338.31,-227.8 337.95,-226.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"341.42,-225.5 336.05,-216.37 334.56,-226.86 341.42,-225.5\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x14e9c0750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'unobserved': [xk_mu ~ Normal(0, 1),\n",
+       "  xk_unobserved,\n",
+       "  beta_sigma ~ Gamma(10, f()),\n",
+       "  beta_j ~ Normal(0, beta_sigma),\n",
+       "  beta_k ~ Normal(0, beta_sigma),\n",
+       "  epsilon ~ Gamma(50, f()),\n",
+       "  xk ~ Unknown(f(xk_observed, xk_unobserved))],\n",
+       " 'observed': [xk_observed,\n",
+       "  yhat ~ Normal(f(beta_j, beta_k, xk_observed, xk_unobserved), epsilon)]}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "point={'xk_mu': array([0., 0.]), 'xk_unobserved': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0.]), 'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0.]), 'beta_k': array([0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n",
+      "point={'xk_mu': array([0., 0.]), 'xk_unobserved': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+      "       0., 0., 0., 0., 0., 0., 0.]), 'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0.]), 'beta_k': array([0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(pm.model_to_graphviz(mdla, formatting=\"plain\"))\n",
+    "display(dict(unobserved=mdla.unobserved_RVs, observed=mdla.observed_RVs))\n",
+    "assert_no_rvs(mdla.logp())\n",
+    "mdla.debug(fn=\"logp\", verbose=True)\n",
+    "mdla.debug(fn=\"random\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ We have two new auto-created nodes `xk_observed` (observed data) and `xk_unobserved` (unobserved free RV), which \n",
+    "  each have same distribution as we specified for `xk`\n",
+    "+ The original `xk` is now represented by a new `Deterministic` node with a function of the two (concatenation and reshaping)\n",
+    "+ In particular note `xk_unobserved` has a new, flattened shape with length equal to the count of NaNs in the relevant \n",
+    "  features $k$, and it loses the `dims` that we assigned to `xk`. This is an unhelpful current limitation and means we\n",
+    "  have to do some indexing to recover the PPC values later"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Sample Prior Predictive, View Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [beta_j, beta_k, beta_sigma, epsilon, xk_mu, xk_observed, xk_unobserved, yhat]\n"
+     ]
+    }
+   ],
+   "source": [
+    "GRP = \"prior\"\n",
+    "kws = dict(samples=2000, return_inferencedata=True, random_seed=42)\n",
+    "with mdla:\n",
+    "    ida = pm.sample_prior_predictive(\n",
+    "        var_names=RVS_PPC + RVS_PRIOR + RVS_K + RVS_XK + RVS_XK_UNOBS, **kws\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.1 In-Sample Prior PPC (Retrodictive Check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 237,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_ppc_retrodictive(ida, grp=GRP, rvs=[\"yhat\"], mdlnm=\"mdla\", ynm=\"y\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Values are wrong as expected, but range is reasonable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Quick look at selected priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Coefficients etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x345.6 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 340,
+       "width": 848
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_krushke(ida, GRP, rvs=RVS_PRIOR, mdlnm=\"mdla\", n=1 + 1 + 3 + 2, nrows=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Model priors `beta_sigma`, `beta_j: (levels)`, `beta_k: (levels)`, `epsilon` all have reasonable prior ranges as specified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Hierarchical values $\\mu_{k}$ for missing data in $\\mathbb{x}_{k}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x201.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 199,
+       "width": 419
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_krushke(ida, GRP, RVS_K, mdlnm=\"mdla\", n=2, nrows=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Data imputation hierarchical priors `xk_mu (levels)` have reasonable prior ranges as specified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The values of missing data in $x_{k}$ (`xk_unobserved`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x561.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 551,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_forest(ida, GRP, RVS_XK_UNOBS, \"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Prior values for the auto-imputed data `xk_unobserved` are of course all the same, and in reasonable ranges as specified\n",
+    "+ Note again that this is a flattened RV with length equal to the count of NaNs in features `c` and `d`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3 Sample Posterior, View Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3.1 Sample Posterior and PPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [xk_mu, xk_unobserved, beta_sigma, beta_j, beta_k, epsilon]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e059a01871b45038fa0777540043661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 3_000 tune and 500 draw iterations (12_000 + 2_000 draws total) took 181 seconds.\n",
+      "Sampling: [xk_observed, yhat]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86693b4c3ff54846bf88c6267e97d4b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GRP = \"posterior\"\n",
+    "with mdla:\n",
+    "    ida.extend(pm.sample(**SAMPLE_KWS), join=\"right\")\n",
+    "    ida.extend(\n",
+    "        pm.sample_posterior_predictive(trace=ida.posterior, var_names=RVS_PPC),\n",
+    "        join=\"right\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3.2 View Traces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>epsilon</th>\n",
+       "      <td>1.060</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>0.834</td>\n",
+       "      <td>1.284</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>1473.0</td>\n",
+       "      <td>1542.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_sigma</th>\n",
+       "      <td>20.614</td>\n",
+       "      <td>0.830</td>\n",
+       "      <td>19.026</td>\n",
+       "      <td>22.100</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>3011.0</td>\n",
+       "      <td>1344.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[intercept]</th>\n",
+       "      <td>281.014</td>\n",
+       "      <td>0.298</td>\n",
+       "      <td>280.466</td>\n",
+       "      <td>281.596</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>1578.0</td>\n",
+       "      <td>1491.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[a]</th>\n",
+       "      <td>8.567</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>7.883</td>\n",
+       "      <td>9.285</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>1181.0</td>\n",
+       "      <td>1416.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_j[b]</th>\n",
+       "      <td>-55.839</td>\n",
+       "      <td>0.354</td>\n",
+       "      <td>-56.470</td>\n",
+       "      <td>-55.147</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>1476.0</td>\n",
+       "      <td>1375.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_k[c]</th>\n",
+       "      <td>23.532</td>\n",
+       "      <td>0.355</td>\n",
+       "      <td>22.854</td>\n",
+       "      <td>24.207</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>937.0</td>\n",
+       "      <td>1111.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_k[d]</th>\n",
+       "      <td>47.316</td>\n",
+       "      <td>0.310</td>\n",
+       "      <td>46.749</td>\n",
+       "      <td>47.879</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xk_mu[c]</th>\n",
+       "      <td>0.108</td>\n",
+       "      <td>0.204</td>\n",
+       "      <td>-0.253</td>\n",
+       "      <td>0.498</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1649.0</td>\n",
+       "      <td>1434.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xk_mu[d]</th>\n",
+       "      <td>0.081</td>\n",
+       "      <td>0.187</td>\n",
+       "      <td>-0.278</td>\n",
+       "      <td>0.415</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2204.0</td>\n",
+       "      <td>1478.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      mean     sd   hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "epsilon              1.060  0.121    0.834    1.284      0.003    0.002   \n",
+       "beta_sigma          20.614  0.830   19.026   22.100      0.015    0.011   \n",
+       "beta_j[intercept]  281.014  0.298  280.466  281.596      0.008    0.005   \n",
+       "beta_j[a]            8.567  0.370    7.883    9.285      0.011    0.008   \n",
+       "beta_j[b]          -55.839  0.354  -56.470  -55.147      0.009    0.007   \n",
+       "beta_k[c]           23.532  0.355   22.854   24.207      0.012    0.008   \n",
+       "beta_k[d]           47.316  0.310   46.749   47.879      0.007    0.005   \n",
+       "xk_mu[c]             0.108  0.204   -0.253    0.498      0.005    0.004   \n",
+       "xk_mu[d]             0.081  0.187   -0.278    0.415      0.004    0.004   \n",
+       "\n",
+       "                   ess_bulk  ess_tail  r_hat  \n",
+       "epsilon              1473.0    1542.0    1.0  \n",
+       "beta_sigma           3011.0    1344.0    1.0  \n",
+       "beta_j[intercept]    1578.0    1491.0    1.0  \n",
+       "beta_j[a]            1181.0    1416.0    1.0  \n",
+       "beta_j[b]            1476.0    1375.0    1.0  \n",
+       "beta_k[c]             937.0    1111.0    1.0  \n",
+       "beta_k[d]            1968.0    1821.0    1.0  \n",
+       "xk_mu[c]             1649.0    1434.0    1.0  \n",
+       "xk_mu[d]             2204.0    1478.0    1.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x453.6 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 446,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x115.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 116,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_traces_and_display_summary(ida, rvs=RVS_PRIOR + RVS_K, mdlnm=\"mdla\", energy=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Samples well-mixed and well-behaved: `ess_bulk` is good, `r_hat` is good\n",
+    "+ Marginal energy | energy transition looks a little mismatched: whilst `E-BFMI >> 0.3` (and is\n",
+    "  [apparently reasonable](https://python.arviz.org/en/stable/api/generated/arviz.bfmi.html#arviz.bfmi)), note the\n",
+    "  values are lower than for `Model0`. This is an effect of the missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3.3 In-Sample Posterior PPC (Retrodictive Check)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 237,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_ppc_retrodictive(ida, grp=GRP, rvs=[\"yhat\"], mdlnm=\"mdla\", ynm=\"y\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ In-sample PPC `yhat` tracks the observed `y` moderately well: slightly overdispersed, perhaps a likelihood\n",
+    "  with fatter tails would be more appropriate (e.g. StudentT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3.4 In-Sample PPC LOO-PIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x172.8 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 172,
+       "width": 843
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_loo_pit(ida, \"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ `LOO-PIT` looks good, again slightly overdispersed but acceptable for use"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3.5 Compare Log-Likelihood vs Other Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jon/miniforge/envs/oreum_survival/lib/python3.11/site-packages/arviz/stats/stats.py:795: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.70 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  warnings.warn(\n",
+      "/Users/jon/miniforge/envs/oreum_survival/lib/python3.11/site-packages/arviz/stats/stats.py:795: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.70 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rank</th>\n",
+       "      <th>elpd_loo</th>\n",
+       "      <th>p_loo</th>\n",
+       "      <th>elpd_diff</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>se</th>\n",
+       "      <th>dse</th>\n",
+       "      <th>warning</th>\n",
+       "      <th>scale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mdl0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-45.658024</td>\n",
+       "      <td>4.860332</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.525409</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mdla</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-59.472497</td>\n",
+       "      <td>18.295350</td>\n",
+       "      <td>13.814473</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.059752</td>\n",
+       "      <td>3.259371</td>\n",
+       "      <td>True</td>\n",
+       "      <td>log</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rank   elpd_loo      p_loo  elpd_diff  weight        se       dse  \\\n",
+       "mdl0     0 -45.658024   4.860332   0.000000     1.0  3.525409  0.000000   \n",
+       "mdla     1 -59.472497  18.295350  13.814473     0.0  3.059752  3.259371   \n",
+       "\n",
+       "      warning scale  \n",
+       "mdl0     True   log  \n",
+       "mdla     True   log  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x187.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 185,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_compare_log_likelihood(idatad: dict, yhat: str = \"yhat\") -> plt.Figure:\n",
+    "    \"\"\"Convenience to plot comparison for a dict of idatas\"\"\"\n",
+    "    dfcomp = az.compare(idatad, var_name=yhat, ic=\"loo\", method=\"stacking\", scale=\"log\")\n",
+    "    f, axs = plt.subplots(1, 1, figsize=(12, 2 + 0.3 * len(idatad)))\n",
+    "    _ = az.plot_compare(dfcomp, ax=axs, title=False, textsize=10, legend=False)\n",
+    "    _ = f.suptitle(\n",
+    "        \"Model Performance Comparison: ELPD via In-Sample LOO-PIT: `\"\n",
+    "        + \"` vs `\".join(list(idatad.keys()))\n",
+    "        + \"`\\n(higher & narrower is better)\"\n",
+    "    )\n",
+    "    _ = f.tight_layout()\n",
+    "    display(dfcomp)\n",
+    "    return f\n",
+    "\n",
+    "\n",
+    "f = plot_compare_log_likelihood(idatad={\"mdl0\": id0, \"mdla\": ida})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Very interesting: our auto-imputing `ModelA` does of course suffer in comparison to `Model0` which has the benefit \n",
+    "  of the complete dataset (no missing values), but it's not that much worse, and (of course) we've been able to handle\n",
+    "  missing values in the in-sample data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4 Evaluate Posterior Parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4.1 Coefficients etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x345.6 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 340,
+       "width": 847
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_krushke(ida, GRP, RVS_PRIOR, mdlnm=\"mdla\", n=1 + 1 + 3 + 2, nrows=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Model coeffs `beta_sigma`, `beta_j: (levels)`, `beta_k: (levels)`, `epsilon` all have reasonable prior ranges as specified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For interest's sake forestplot the `beta_j` and `beta_k` levels to compare relative effects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x151.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 149,
+       "width": 851
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_forest(ida, grp=GRP, rvs=[\"beta_j\", \"beta_k\"], mdlnm=\"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Very tight and distinct posterior distributions\n",
+    "+ Loosely compare this to the same plot for `Model0` in $\\S1.4$ above: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4.2 Hierarchical values for missing data in $\\mathbb{x}_{k}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x201.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 199,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_krushke(ida, GRP, RVS_K, mdlnm=\"mdla\", n=2, nrows=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Data imputation hierarchical priors haven't moved far from `0`, which is reasonable because the missingness is at random\n",
+    "+ However, they do show slight differences which is encouraging of them picking up the inherent differences in the \n",
+    "  primary raw data due to `REFVALS_X_MU`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4.3 View auto-imputed values of missing data in $x_{k}$ (`xk_unobserved`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 864x561.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 551,
+       "width": 852
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plot_forest(ida, GRP, RVS_XK_UNOBS, \"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ We have used our model to autoimpute missing values in `xk_unobserved`, with quantified uncertainty\n",
+    "+ With the appropriate post-model indexing, we can use these as posterior predictions of the true values of the \n",
+    "  missing data\n",
+    "+ We'll show that indexing and a special comparison to the synthetic known values next"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4.4 In-sample: Compare auto-imputed values $x_{k}$ `xk_unobserved` to known true values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE**\n",
+    "\n",
+    "+ We can only compare because it's a synthetic dataset where we created those complete (full) values in `dfraw` above"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create index to extract appropriate obs from `xk`, because `xk_unobserved` doesn't have the right dims indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfx_train_xk = (\n",
+    "    dfx_train.loc[:, [\"c\", \"d\"]]\n",
+    "    .reset_index()\n",
+    "    .melt(id_vars=[\"oid\"], var_name=\"xk_nm\", value_name=\"xk\")\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    ")\n",
+    "idx_xk_unobs = dfx_train_xk.loc[dfx_train_xk[\"xk\"].isnull()].index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Extract from PPC idata , isolate via index, transform back into data domain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>chain</th>\n",
+       "      <th>draw</th>\n",
+       "      <th>xk</th>\n",
+       "      <th>mn</th>\n",
+       "      <th>sdev</th>\n",
+       "      <th>xk_unobs_ppc_data_domain</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th>xk_nm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">o03</th>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.331594</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.927788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.263749</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.873796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.673567</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>10.199937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.348009</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.940851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.751358</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>11.057664</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           chain  draw        xk        mn      sdev  xk_unobs_ppc_data_domain\n",
+       "oid xk_nm                                                                     \n",
+       "o03 c          0     0  0.331594  9.663899  0.795819                  9.927788\n",
+       "    c          0     1  0.263749  9.663899  0.795819                  9.873796\n",
+       "    c          0     2  0.673567  9.663899  0.795819                 10.199937\n",
+       "    c          0     3  0.348009  9.663899  0.795819                  9.940851\n",
+       "    c          0     4  1.751358  9.663899  0.795819                 11.057664"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_mns_sdevs = pd.DataFrame({\"mn\": MNS, \"sdev\": SDEVS}, index=[\"a\", \"b\", \"c\", \"d\"])\n",
+    "df_mns_sdevs.index.name = \"xk_nm\"\n",
+    "\n",
+    "df_xk_unobs = (\n",
+    "    az.extract(ida, group=GRP, var_names=RVS_XK)\n",
+    "    .to_dataframe()\n",
+    "    .drop([\"chain\", \"draw\"], axis=1)\n",
+    "    .reset_index()\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    "    .loc[idx_xk_unobs]\n",
+    ")\n",
+    "df_xk_unobs = (\n",
+    "    pd.merge(df_xk_unobs.reset_index(), df_mns_sdevs.reset_index(), how=\"left\", on=[\"xk_nm\"])\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    "    .sort_index()\n",
+    ")\n",
+    "df_xk_unobs[\"xk_unobs_ppc_data_domain\"] = (df_xk_unobs[\"xk\"] * df_xk_unobs[\"sdev\"]) + df_xk_unobs[\n",
+    "    \"mn\"\n",
+    "]\n",
+    "df_xk_unobs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Attach real values (only available because synthetic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>chain</th>\n",
+       "      <th>draw</th>\n",
+       "      <th>xk</th>\n",
+       "      <th>mn</th>\n",
+       "      <th>sdev</th>\n",
+       "      <th>xk_unobs_ppc_data_domain</th>\n",
+       "      <th>xk_unobs_true_val</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th>xk_nm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">o03</th>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.331594</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.927788</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.263749</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.873796</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.673567</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>10.199937</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.348009</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.940851</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.751358</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>11.057664</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           chain  draw        xk        mn      sdev  \\\n",
+       "oid xk_nm                                              \n",
+       "o03 c          0     0  0.331594  9.663899  0.795819   \n",
+       "    c          0     1  0.263749  9.663899  0.795819   \n",
+       "    c          0     2  0.673567  9.663899  0.795819   \n",
+       "    c          0     3  0.348009  9.663899  0.795819   \n",
+       "    c          0     4  1.751358  9.663899  0.795819   \n",
+       "\n",
+       "           xk_unobs_ppc_data_domain  xk_unobs_true_val  \n",
+       "oid xk_nm                                               \n",
+       "o03 c                      9.927788           9.618262  \n",
+       "    c                      9.873796           9.618262  \n",
+       "    c                     10.199937           9.618262  \n",
+       "    c                      9.940851           9.618262  \n",
+       "    c                     11.057664           9.618262  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfraw_xk = (\n",
+    "    dfraw[[\"c\", \"d\"]]\n",
+    "    .reset_index()\n",
+    "    .melt(id_vars=[\"oid\"], var_name=\"xk_nm\", value_name=\"xk_unobs_true_val\")\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    ")\n",
+    "\n",
+    "df_xk_unobs = pd.merge(\n",
+    "    df_xk_unobs, dfraw_xk.loc[idx_xk_unobs], how=\"left\", left_index=True, right_index=True\n",
+    ")\n",
+    "df_xk_unobs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Force dtypes for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>chain</th>\n",
+       "      <th>draw</th>\n",
+       "      <th>xk</th>\n",
+       "      <th>mn</th>\n",
+       "      <th>sdev</th>\n",
+       "      <th>xk_unobs_ppc_data_domain</th>\n",
+       "      <th>xk_unobs_true_val</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oid</th>\n",
+       "      <th>xk_nm</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">o03</th>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.331594</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.927788</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.263749</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.873796</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.673567</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>10.199937</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.348009</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>9.940851</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.751358</td>\n",
+       "      <td>9.663899</td>\n",
+       "      <td>0.795819</td>\n",
+       "      <td>11.057664</td>\n",
+       "      <td>9.618262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           chain  draw        xk        mn      sdev  \\\n",
+       "oid xk_nm                                              \n",
+       "o03 c          0     0  0.331594  9.663899  0.795819   \n",
+       "    c          0     1  0.263749  9.663899  0.795819   \n",
+       "    c          0     2  0.673567  9.663899  0.795819   \n",
+       "    c          0     3  0.348009  9.663899  0.795819   \n",
+       "    c          0     4  1.751358  9.663899  0.795819   \n",
+       "\n",
+       "           xk_unobs_ppc_data_domain  xk_unobs_true_val  \n",
+       "oid xk_nm                                               \n",
+       "o03 c                      9.927788           9.618262  \n",
+       "    c                      9.873796           9.618262  \n",
+       "    c                     10.199937           9.618262  \n",
+       "    c                      9.940851           9.618262  \n",
+       "    c                     11.057664           9.618262  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_xk_unobs = df_xk_unobs.reset_index()\n",
+    "df_xk_unobs[\"oid\"] = pd.Categorical(df_xk_unobs[\"oid\"])\n",
+    "df_xk_unobs[\"xk_nm\"] = pd.Categorical(df_xk_unobs[\"xk_nm\"])\n",
+    "df_xk_unobs.set_index([\"oid\", \"xk_nm\"], inplace=True)\n",
+    "df_xk_unobs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot posterior `xk_unobserved` vs known true values (only possible in this synthetic example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Nmzbh8uXLiIiIkNoL3bt3R/Xq1TWmw5g2nKIrV65g7dq1uHHjBuLj41GwYEHUr18fffr00ViP1rcNaCzFOqhIzGcUz6/42YIFC5A7d2789ddfePToERwdHVGqVCmsWLECx48fl7a1a9cuVKxYUe0+mzZtiqdPnyrdj2kZUh4YY/369fjrr78AaC73RX369MHFixdRtGhRHD9+XOk7Q+tD5mKqMtrQ+KY2psqDExMT4e3tjaNHjyIoKAifPn2Ci4uLVEdu1qyZxviAMTFfTUwZczVnvUhX+tYLNTl58iQ2b96Me/fuITk5GUWLFsWPP/6IHj16aMzTjP19zHH+GjRogKNHj1rknhLLSNGlS5eka2jz5s2oUaOGUvoXLFiA5s2bq6TL0FiNqWJFpnLt2jXs2rULV65cQWhoKKytrVGwYEHUrl0bvXr1Qr58+VTW0eX8GBvXMIVVq1YBAFq2bKn1BcKQkBBs2rQJ586dw6tXr2Bra4ty5cqhZ8+eqFevnlTepq0viflvp06dMGDAAMyYMQOBgYGQy+UoUqQIpk2bplQOGtPGMHT9jHr+Z4o2kKnbgIpl3ZkzZ+Dt7Y0bN24gPDwcdnZ2+Prrr1G9enV06dIFJUuW1Jiu5s2bY+7cuQgODsaJEyfQrFkznc9LRhOPf/HixahXrx7++ecfHD16FC9fvoStrS1KliyJ7t27S8cQFRWFtWvXws/PD2/evIGjoyMqVqyIgQMHao2FXLlyBZs3b8adO3cQGhqKfPny4ccff8SAAQOMSn9ERAR27dqFixcv4unTp4iMjISNjQ3c3NxQsWJFtG3bVqc4OOmGnS3M5Pnz5xg+fLiUkYlu3bqFW7duwdfXFxs3btQatNEkKCgIffr0QXh4uNLnoaGhCA0NxdmzZ7F161b8888/Kg9bg4OD0b9/f7x69Uplu4rr79u3Dxs3btT44CMwMBCTJ09GdHS00vonTpzA6dOnsX79ekRGRmLs2LGIj4+XlgkJCcG+ffukfairYADA7du3MXXqVHz8+FH67Pnz53j+/DkOHjyIKVOm6N3r7O7duxg0aJDKb/L+/XscP34cx48fR/PmzTFnzhzY2dnptW1dpaSkYNSoUThy5IjS59evX8f169exY8cObNiwQW1hfuXKFYwaNQohISFKn4eGhuJ///sf/ve//6F69epYvHix0vQIf/zxB65evYq3b99i+fLlaNy4sVIjOjo6GmPHjkVycjJcXFzg5eWltkFy6tQpDBs2TGnIoCdPnuDJkyfYs2cPvLy89H6o4Ofnh99//13pdwZS52V9+/Ytjhw5gmbNmmHOnDk6BY0UHTp0CBMnTkRSUpLS569fv8br16/h4+ODn376CfPnz9e7B2VISAiGDx+O69evK30eGRmJK1eu4MqVK9i0aROWLVuGsmXL6rVtfezcuRPTp09HSkqK9NnDhw/x8OFD7Ny5E6tWrVJbkBuT/nfv3qFXr1549uyZ0ufh4eEIDw/HpUuXsHHjRmzcuFFtp4T0rF69GosXL5ZG/xA9fvwYjx8/xo4dOzBr1iy0aNFC4zZ8fHyUgmNhYWFwcHDQqaPDokWLsHr1aqVzCgBPnz7F06dPsWvXLowYMQL9+/c3y/7TI5fLMW/ePLRu3RoxMTGYNWsWatWqpXTPv337Vgpiu7u7Y+bMmTpte+/evVJHi8qVK2Pt2rWZckjA+Ph49O7dG4GBgUqfP336FOvXr8f+/fuxdetWtcFCU+Q5cXFx6N27Ny5fvqyUpoCAAAQEBGD37t1YvXq1SicVc+ZJujI2Hzd0/ZiYGEyZMkV6+Cv6/Pkzbt68iZs3b2L37t3YsGED3N3dTXCk/9m4cSPmzZundE+/ffsWu3btwtGjR7XmJQkJCZgyZQoOHDig8l1kZCQiIyNx9+5dbN++HWvWrEHVqlX1Spu5t6+LI0eOYMKECfj8+bP0WXx8PA4fPozjx4+je/fuWtc3tG6iKCkpCUOGDIGfn5/S5w8fPoSXlxcuXbqEpUuXon///rh06ZLSMuL18+zZM2keWpEgCPDy8sK6detU5nr/9OkTPn36hEePHmHXrl34+++/DWrgnzx5EqNHj1aq6yYkJODs2bM4e/YsunfvLnWMMJX69etDJpNBEAScO3dO5aGLIAjw9/eX/h8QEKDS2SIxMREXLlwAAJUHv8aaN2+eysPTd+/e4ciRIzh+/DimT5+ODh066L3dx48fY9iwYQgODlb6PCIiAhcuXMCFCxewefNmrFixQur4ZWtri5YtW2LLli24cOGC1PFbnUOHDgEAcuTIgR9//FH6PDk5GbNnz8bWrVuVlv/8+TPu3buHe/fuYdu2bVi0aJHWh7pr1qzB5s2bpf+LD5HTWrhwoRTEFN29exd3797Fjh07sG7dOhQrVkzjftQxRd3GGMbU7RISEjBx4kSp47goODgYixcvxrFjx1CgQAGD0xYSEoLffvsNd+/eVflOrNsGBgZi586d2LZtm9oA/dq1a7Fw4UKV4wsODkZwcDB2796NpUuXSg/hM8rWrVsxZ84clXpHYmIiYmJi8OzZM3h7e2PChAno3bt3hqWrc+fOOH36NE6dOiV1wmzXrp3SMlOnTpXKlVmzZunU0eL169fo2bMn3r17B0dHR6xZswbVqlUzyzEYa9u2bZg1a5bSNRMWFibFJMaNG6c2iG6qNuiWLVuwZs0apbJR8X5cvnw5vvvuO6V1zN0GNIXLly9j7969SExMBJCafyQnJ5tsNElTlQf6at26NRYsWICkpCQcPnxYY2eLkJAQBAQEAIBSJ9SMqA8ZwhRltKnim+bw8uVL/Pbbb3j06JHS5+Hh4Th9+jROnz6NnTt3YsmSJSr1AWNivroyNOaa0fUiTQypF6qjrt4qHsf27duxbt06lY4nxvw+5jx/bm5uWfqeMkWsxphYkSkkJSXhjz/+wN69e5U+//z5M4KCghAUFIRt27Zh+vTpWl8WUMeYuIapBAcHS+WMus4gIn9/fwwePBgxMTHSZ7GxsTh//jzOnz+PPn36pLuviIgIdO/eHW/evJE+e/r0qVIdw9j4sSniz+Z6/meKNpA524DTpk3Djh07lD5LTEzEo0eP8OjRI2zfvh2TJk1Cjx491K7v6OiIH374AT4+Pti+fXum7mwhev/+PX7++WelunBcXJxUB3/58iUaN26MPn364O3bt9IyCQkJOH36NM6fP49Vq1apvOgtCAL++usvlbLo5cuXWL9+PY4cOaK1I7Y2x48fx4QJExAbG6v0eWJiImJjY/Hq1Sv4+PigV69e+P333w3aBylT/+olGW3atGmIiIjA0KFDcfToUfj7++Off/5B6dKlAaQGhzdu3GjQtkePHo3w8HC4urpi5syZOHHiBAICAnDw4EF069YNQGqAes6cOUrrxcfHS5UmOzs7jB49Gr6+vggICMCxY8fg5eUlFVrXr19XqfwpGj9+PKysrDBt2jScOnUKJ0+exNChQyGTyZCQkIAJEyZgzJgxcHd3x/Lly+Hv748TJ06gc+fOAFIraWkDiYp+//13xMTE4Ndff8Xx48dx8eJFLFq0CO7u7khOTsa0adNw8uRJnc/Zy5cv0bt3b7x79w65cuXClClTcPLkSQQGBmLPnj1S0NfX11fl7QlRsWLFUKxYMYNHcABSC+EjR46gXLly2LBhAwICAuDr6ys9zAgODsbAgQOlAIHo/v376N+/P0JCQuDo6IiRI0fi6NGjUvpbt24NILX3cL9+/ZQemri4uGDu3LnSbzNp0iSlgvbPP/+UKtJ//vmnxgddo0aNglwux+jRo/G///0P586dw8yZM+Hq6or4+HgMGzZMbaBSk3PnzmHEiBH4+PGj0m/i7++PLVu2oG7dugCAo0ePYuTIkVJgoGrVqrh27RqmT58ubcvX1xfXrl2T3sJ8+/YtJk+ejKSkJJQuXRqrVq3C6dOncfHiRWzdulVqtBw5cgT79u3TOc1AaueUvn37SkGuDh06YM+ePQgMDMSRI0cwdOhQ2NnZ4c2bN+jduzeeP38OIPWh97Vr13Dt2jWpQtSyZUvpM317KoaFheHPP/+Ei4sLpk+fjnPnzuHUqVMYN24cHBwcEBkZiV9++UXlAZih6RdNnToVz549g52dHSZMmICjR48iICAAR44cwZAhQ2BlZYWQkBClQnrAgAG4du2aNAJDgQIFpONW7EG/YcMGLFiwAMnJyahevTrWrFkDf39/nDt3DkuXLoWnpyfi4+MxZswYnDlzRuO52bJlCzw8PLBt2zZcuHABy5cv16kyv2jRIqxcuRIpKSnw9PTE0qVLpcbAokWL8M033yA5ORleXl5q38Izdv+6KlSoECZPngwA+PDhA2bMmCF9l5KSgnHjxuHjx4+wtraGl5eXTiNTHDhwAFOmTIEgCKhevTrWrVuXKTtaAMA///yDwMBAtGrVSrp2Dxw4gKZNmwJIbZQpnhORoXlOWlevXsXly5dRu3ZtbN++HQEBAfD29pYam1evXsWYMWOU1jFnnqQPY/NxQ9efOHGi1NGiYcOG2LZtG/z9/XHkyBH0798fcrkcz549w5AhQ1Qagsbw9fXFnDlzkJKSAg8PDylPOXHiBAYPHoy4uDhs375d4/pLliyROkI0btwYW7dulR6ib9u2TWp0x8TEYNq0adJ6upRTxmzfVK5fv45Ro0bh8+fPKFCgABYsWICLFy/i1KlTmDhxImxsbLTmdcbUTRQdOHAAfn5+aNCgAXbu3ImLFy9iy5YtUhDs3LlzaNu2LS5fvoxffvkFR44cgb+/P1atWiU9gFu7dq1Kebdz506sXbtWKV87ffo0Lly4gD179qBr166wsrJCYmIipk2bpvf8k0lJSRg1ahRy5syJWbNm4cyZMzh37hzmzZsnBYu3bt2KK1eu6LXd9OTNm1fqPCF2mFB0//59RERESP8XA2KKrl69ik+fPgFIv7PFtWvXlDpKTZ8+XeUzUUJCAjZs2ICSJUti0aJFOHv2LI4dO4bRo0fDzs4OycnJmDlzJt6/f6/bwf6/d+/eoU+fPggODoa1tTX69u2LQ4cOITAwEAcPHkSvXr0gl8vx8OFD9OrVCx8+fJDWFd++SUpKwtGjR9VuPyEhQXpztVmzZkqBY8WAeKNGjbB161YEBATg9OnTmDt3Ltzd3REZGYmBAwdqHV5z8+bNqFatGvbv349z587h77//Vhl6986dO1i1ahW+/vpr6X48ceIEBg4cCCsrK7x69Qq9e/dWCZZoY2jdJr26W0bV7WbOnCkFGRs3biyV+/v27UOLFi0QFBSEU6dO6Xw+FAmCgOHDh+Pu3buQyWTo168fDhw4AH9/f5w8eRIrVqzAt99+CyA1KKn4lpdo27ZtmD9/PpKTk+Hh4SGd31OnTmHGjBlwdXVFbGwshg0bhjdv3kjtAcW3TdesWYNr165h7dq1Bh2HOufOncOMGTOQlJQET09PLFu2DP/++y/8/f2xf/9+DBw4EPb29hAEAfPnz1fJP81t1qxZUseVuXPnIjQ0VPpu79690hv53bp106kz/9u3b9GzZ0+8efMGOXLkwPr16zNtR4u3b99i5syZKFKkCBYtWoQLFy7gzJkzmDRpkpT3LFiwAC9fvlRaz9g2nKLVq1cjT548mDNnjnS9Tpw4EY6OjoiOjsZvv/2msn9D2oDGatWqlUo7WVvbeceOHcidOzfWrFmDixcvYuPGjRg+fLjJ0mOq8kBfefLkkdopR44cUXkoJPLx8UFKSgrkcrnSQzxz14eMYUwZbcr4pqlFRkaiV69e0ggrI0eOxJEjR3Dp0iUcPHgQffv2hbW1NQIDAzFo0CCVTnGGxnz1YWjMNSPrRZoYUy9UJNZbixYtiqVLl0rtxCFDhsDGxgbv379Hv379VB7+G/P7mPv8Weqe+vPPP3Ht2jVUqVIFAFClShUpv9blhQFTxWoMjRWZysSJE6WOFtWqVcP69evh7++P06dPY9asWciXLx/i4+Mxfvx4vaZbMzauYSqHDx8GkPqcoXLlymqXefr0Kfr374+YmBi4ublhxowZUoxj5syZyJ07N/755x+Vzptp+fn5ITQ0FH/88QfOnz+PAwcOYObMmVKc0tg2hqniz+Z6/mdsG8hcbUAg9T4UO1o0adIEO3fuxIULF3D69GksXrwYhQsXhiAImDNnjkqHOEVip4PLly9neFvEEPPnz8eLFy/Qt29fHDlyBOfOncP8+fOlmPuyZcvQt29fREdHY+rUqTh9+jTOnTuHSZMmwcbGBklJSSqj3AKpsSzxN6hatSq2bNmCgIAA+Pj4oEePHnj37h0OHjyod3ofPnyIUaNGITY2Fu7u7pg3bx78/PwQEBCAQ4cOYezYsciZMycAYNOmTbh165YRZ4ckAull3759goeHh+Dh4SHs27dP6buXL19K33l4eAinT59WWT88PFyoWLGi4OHhIbRo0ULv/T98+FDa/v79+9UuM3r0aMHDw0MoV66cEBcXJ32+e/duad29e/eqXffdu3dC+fLlBQ8PD6FHjx5K3wUEBEjrlypVSrh9+7bK+n379pWWqVu3rhAZGamyTJs2bQQPDw+hUaNGGrfv4eEheHt7q6wbEhIi1KpVS/Dw8BDq168vJCYmql3fx8dHab1ffvlF8PDwEL777jvh5cuXao99zZo10vpnzpxRu4yhunfvLm27U6dOQnx8vMoyq1atkpbZuXOn0nddunSRftObN2+q3ceKFSuk9VesWKHy/dy5c6XvN23aJAiCIPj6+kqfTZkyRWUdxeu9dOnSwsWLF1WWefjwofDtt98KHh4eQpcuXTSuf/36denzz58/Cw0aNBA8PDyE6tWrCy9evFDZbkpKijB58mRp/YMHD2rcdtrfdOvWrdJ3r1+/Vtn258+fhUaNGgkeHh5Ct27dVL7XZsGCBSrnMa2AgAChTJkygoeHh9C7d2+V7+vXry94eHgI48eP12vfgiAIS5YskfZfsWJF4eHDhyrLXLx4UShVqpTg4eEhjBkzxmTpj46OFjw9PQUPDw9h6dKlatdV3H7a33X8+PHSvZvWmzdvhLJlywoeHh7C8OHDheTkZJVlYmNjpfyjbt26QkJCgvSdYv5bpkwZtdeUICifv/fv30ufBwcHC6VLlxY8PDyEzp07C7GxsSrrfvr0Sdp/2bJlla47XfdvSkOHDpX2efLkSUEQBGH16tXSZ6tWrVJZR93xHzp0SLpeevfurVRumJK2vEakmP6097V433h4eAjz5s1Tu37Hjh0FDw8PwdPTUwgPD5c+N0Weo7j/oUOHqr1Gp0yZIi1z7tw56XNz5knpMWU+bsj6Z86ckdafNm2a2jQqln9+fn5GHO1/Pn/+LNSuXVvw8PAQmjRpInz69EllmWPHjinVOxSvuejoaKFChQpSfUjd7y0IgjBw4ECNv622csoU2zeWmJ/VqFFDePfuncr3169fl8oCDw8PISAgQOl7Y+sminWjQYMGCSkpKUrf37hxQ+n3Wbt2rcr2//e//0nfHzhwQOm7evXqCR4eHkLTpk015mszZsyQ1r906ZLa9DVp0kTpc8XftXr16sLbt29Vtqt43c+YMUPtvo0hntcyZcqoXNtifbZ69epSXUGxvBQEQZgzZ47aY9OUTyuWcWnbP4LwX/nu4eEhtGzZUoiOjlZZ5p9//pGW2b17t17HK7ZtPD09NeYRBw8elLY/adIkpe9atWoleHh4CB07dlS77okTJ6R1r169Kn1+/fp16fO5c+eqXTc8PFy61jp06KD0nWLbpGrVqmrzIUFQPn916tRRez8qXncLFy5Uu365cuWUPje2bqO4bXV1t/S+N7Zud//+fal+MmrUKJU8QhAE4c8//5TOi6Y0anLx4kVp3cWLF6tdJiYmRmp7NmjQQOk7xXZ9u3bt1J7fq1evSnXnmTNnSp8rXhtp81ZB0Jz/KFK8PtPel2L+XL16daX6kCLFezJtXEHTNWVKivn34MGDBUEQhOfPn0vntGXLliptZsW8aPXq1YIgpMYvxDpU1apVNZZHxtLlnLx//15K35IlS5S+U6yDN27cWG1+sH37dmmZDRs2KH1nbBtUcf+a6sKXL1+W8oxRo0ZJnxvbBjSWYtrVUawrXL58We0ymuISaTVp0kTw8PAQunfvrvS5seWBsfz8/KT9nz17Vu0yLVu2FDw8PIQ+ffoofW6u+pCpGFpGmzK+mTZ+aGweLLYJy5cvrzZuKgjKsbjt27dLnxsT802PsTHXjKoXpcfYeqFivatx48bChw8fVNY/fPiwtIyXl5f0uTG/T0adP0vdU4Lw372TNg9Nm37Fe87UsRp9Y0WmcuHCBSkNQ4cOFZKSklSWCQkJkX7f6tWrK/2G2s6PMXENU/r5558FDw8P4ZdfftG4jPhcqHLlysKTJ09Uvn/+/LlQrVo1jfUlxRjBsmXL1O7D2DaGKePPHh6mf/5nbBvI3G1AMRbdqFEjtefu6dOnUr1RUxtLEFKvBfEYdu3apXE5S1P8rf/55x+V7xXj2KVKlRJu3LihsoziMznF+NH79++lWGr37t1V4jaCIAjr169XSoOm9KWN4YwZM0bw8EiNGam7FwVBuTzQVMcn/XBkCzMpV66c2rnX3dzcpJ6e6oblSo9iL/OwsDC1ywwfPhzr16/HoUOHlOaF+uqrr9CjRw80b95cZR5iUb58+aSeqpp6/wJAvXr1UK5cOZXPFYe16dy5s9RDSpHY+zHtEEuKatWqhTZt2qh8/tVXX2HYsGEAUocKvXr1qsZtiJ49e4bz588DAH799VcULFhQ7XK//PKLNKqDOXuE/vHHH2qnKenXr5+0f8U3m4OCgqTj7Nmzp8ZhIwcOHAgPDw8AqW9ZpTVy5Ejp+4ULF+LWrVvSW7clSpRI9y2Udu3aqR3GrmTJkujVqxeA1Dcldbmuz58/Ly03fPhwtaOFyGQyTJ48WRr2Tp83EdK7T2xtbeHl5YWtW7fCy8tL5+0KgiDNa/7tt99K8+mlVaNGDWkUl4sXL2rtyWmMAQMGqJ0DrWbNmvjpp58AACdOnJCGNzc2/YmJiVIP8rRDJop69OiBNWvWwNfXV6chf0U7d+5EYmIi5HI5Jk+eDLlctXhycHDA2LFjAaTmH5p68ZYvX17vEWh2794tvSU0Y8YMtcMS5siRQ3orPTExETt37jTZ/g0xffp05M2bF0DqmwRXr17FkiVLAKReA/369Ut3G0eOHMH48eORkpKCunXrYtWqVRk6vKohrK2tMXDgQLXfiW9oC4KA169fS5+bMs+xsbHBH3/8ofYaHTdunDQ8oGI+bq48SV/G5uOGrC/2wHZ2dsa4cePUbrdHjx4oUaIEvv/+e42jH+grICBAent++PDhakdqadKkicZh3aOjo9GrVy+0aNECgwYNUvt7A1AaZltbvSmjt5+e4OBgaRSSX3/9Ve20bhUrVlQZ2l1kqrqJ6LffflOZaunbb7+V6kuOjo5qyyzFqbIU34aIjo5GmzZt0Lp1awwdOlRjvmbs+W3btq3asq5WrVpSXpD27WBTaNCgAYDUt9bSjlwhjnYhDtkZGxur8paCWH6aegoRABg2bJjaIdvFN8oAaH3jOq2PHz9K0+81bdpU41vurVq1Qv369QGkjpaiONWh+JbfjRs38OLFC5V1xXyqaNGiSm9piVOCubq6YuTIkWr36+bmhsGDBwNIfXNJ0+hAdevW1WnEqLFjx6q9H9u2bStd7+LQz+kxZd3GEMbW7cS3tK2srDB+/Hi107GNGTMGLi4uBqXPzs4OvXv3luZlV8fR0VEa3SJtHuHn5yeNMjJx4kS157dy5cr48ccfUblyZZNNZZAeQRBQp04dtG3bFkOGDNE4hLi5yhdd1a9fH506dQKQei5PnDiB8ePHIzY2Fg4ODli4cGG6U3u+f/8ePXv2xIsXL+Dq6oqNGzdqLI8yk169eqnNDxo3biz9rVh2mLoNOmjQILV14apVq0ojtR0/flzKR83ZBjSlvHnzmmXKNcC05YEh6tWrJ93L4pvFisTh6QEoxdEyqj5kDEPLaFPGN00pOjpaGrmuffv2auOmAPDTTz9JsWHF+KMxMV99GBJztUS9KC1T1AsVjRs3Tu30JS1atJB+H29vb+lzY36fjDp/We2eMmWsxpBYkamIb/rb29tj+vTpaqeG/eqrr6TRtCMjI9Xm52kZG9cwldjYWKmcSTu1jigkJER6/tK3b1+1Ux8WLlwYgwYN0mmfYlw7LWPbGKaMP5vj+Z+xbSBztwHFfDAuLk7tiItFixbF6tWrsWfPHilOqE6hQoWk9lHaKfIyIxcXF7XT7Co+C/3uu++ktqMixbiV4vPQkydPIi4uDkBqHMDGxkZl3T59+ug9jSiQGs/r2LEj+vXrp3F9xfpfZGSk3vsgVexsYSaKN1Fa4sMx8WbSR4kSJZArVy4AgJeXF8aMGYPjx48rDWtWqFAh1K5dG8WKFVMqMOrVq4fJkydjwYIFam/e+Ph4XL58WUpX2qHsFGk6PsVgjqZGhVgx0DY8obb5sBQr0xcvXtS4nEgxCF26dGnExMSo/RcfH4+KFSsCgMmHfBYVKVJEGkoqLblcLhXQd+7ckeY2U0y/pooGkFr5FIMjoaGhKsEVW1tbzJ8/H7a2toiNjUXXrl0RGRkJOzs7LFy4MN0HrNp+E8Vgvb6/ibZ5uezs7KTfW/GcpEdx2Ng+ffpg3rx5CAgIULrmKlSogGrVqqkNaGsSFBQkNSjSm09Mcf66tPMFmoou90l8fLzUQDY2/a6urtJDs+3bt2PgwIE4ePCgUiMzT548qFevHkqUKKE2n9FEvCYKFiwIBwcHjfdpqVKlpO1eunRJ7bbKlCmj837T7t/DwwMlSpTQuFy5cuVQtGhRAJp/V0P2b4hcuXJhzpw5kMlk0jzKiYmJcHNzw19//aXx4a3oxIkTGDt2LJKTk1GkSBEsX7483aB2ZvDNN99obFSI5SsAqZMRYNo8p1q1amrnbAdSyzcx/1G8Ps2VJ+nL2HzckPXFB7/ff/+9xrlVHR0d4evri3/++Ufr3J/68Pf3B5BaNorDjqqjKUCXL18+jBo1Cl5eXirzlgOpQZonT57g8ePH0mfa6k0Zvf30iOcHAH744QeNy2k6P6aqmwCpASlNdSOxXunh4aE2mKw4TZJiR50cOXJg+PDh+OuvvzSm79WrV0oBTEPOr6b6sJWVlVRfN6S+nx5PT0+p87AY0AJS871r164BSC3HxfJK8fd6/vy5NGyrLsPz60vTkLKK+bOu9TkgNS8Vg0XarjXgv7pLYmKi0sOBli1bSnWHtEHNqKgoaWjYtA8dxHLe09MTiYmJGusmig93L1++rDZtutQNbGxs0KRJE43fi7/X69ev0x16FzBt3cYQxtbtxLKkTJky+Oqrr9Tuw8HBweDgcuXKlTFx4kQsXbpUul8VJSYm4tatW9LD5bR5hFi+ubq6SsFUdZYsWYIdO3ZgxIgRBqVTXzKZDL/99hvmzJmjsRPJ+/fvpbwCgMoUlhllwoQJ0rU3atQoKU2TJk1Kdz71sLAw9OrVS7oXFi9ejLJly5ozuSYjxhzSyp07t1R/Vyw7TN0GVez8lpbYmS8xMVH6PczZBjQlc/7+piwPDGFjYyM9/PTz81OpW4gPT3PkyKHUaSej6kPGMLSMNmV805SuX78u1UnLlCmj8VqJiYmR6kyPHj2SHnIYE/PVhyEx14yuF6ljinqhyN7eXms7SMwPFdswxvw+GXX+sto9ZcpYjSGxIlMRf9+aNWuqrVeKGjZsKMXfdal3GxvXMJVnz55J956mOtrZs2elzpna0pNeXQZIjRFpekBsbBvDlPFnczz/M7YNZO42oNi5IDQ0FG3atMGaNWtw//59pel96tWrhwoVKqh9CVskk8mka8lcL6maUoUKFWBtba3yuWJ8WNOzUE1xK/H+dnNz09hhXCaTGXR/d+vWDTNmzNDYBv3w4YPS726p9uCXRvUKIZPQVrCKGbViJiQIgtb5d21sbGBraws7OzvMmDEDI0eORGJiIg4fPozDhw/DysoKZcuWRa1atdCgQYN03+i4efMmbt68iRcvXuDly5d49uwZXr58qTT/o6BhDjQAGt+QUaxIauplq653Z1qaekmK+86ZMyeioqLw9u3bdLel+PZcnz590l0eAD59+oRPnz4pZYamIPYA1kQs5JKTk/Hu3TsUL15c6Ri1FZJpv3/9+rVKBcjT0xOjRo3C3LlzpUx04sSJUvBEG22/ieJx6fKbiMvkyZNH670C/FeJUzwn6SlXrhx++eUXbNiwAdHR0diwYQM2bNgABwcHVKlSBXXq1EHDhg31Hn3gzZs3KunSRPG3UFzPVJycnDSO0gJAqVIqnm9TpH/GjBno06cPYmNjcerUKZw6dQoymQweHh6oVasWfvjhB1SrVk3vRr8YKH3x4oXGBzVpabrW0rumtG0rvXsMSD13z5490/i7GrJ/Q9WpUwfdu3fHli1bkJiYCJlMhrlz52qskCuaMWOGlM8/f/4cgYGB0px5mZmm8geA0sPYlJQU6W9T5jnp3TtFixbFmTNnEBYWhs+fP8POzs5seZK+jM3H9V3/8+fP0sMBsXzLKIq/uba3iXUpU0JCQuDv74+nT59K9aYnT56oBHi01ZssuX11FH/jwoULa1xO0/kxZd3E1dVVY91QLEuMqVNGRUXh/PnzCA4OxosXL/Dq1Ss8efIEUVFRSssZcn51yY9M+bspql+/PrZs2SI98AVSOwt//vwZ+fPnR7FixVCtWjU8e/YMAQEB0lty4ls5efPm1fjAz1ByuVxjHqv4W2mab14dxWvN0LqLm5sb6tevjxMnTuDw4cPSuQBS55xNSEhQmeM+JiYGoaGhAFKDT7rWTYypGxQrVkzrG6pp63bp5aumrNsYwti6nfjWT5EiRbSuo0s+np6goCBcu3ZNahO/ePECz549Uwo6pb2XxfQZ8qZRRomLi8P58+fx6NEjvHz5UipfNI1OkNEcHR3x999/o0uXLtK5btasGTp06JDuups3b1b6TXbv3q2282JmpKnskMlksLa2RkJCgtKxmbIN6uTkpLVjr+L1rPjmr7nagKZkrjaYqcsDQ7Vt2xYbN25EbGws/ve//0kPklNSUqR53X/66SeNL9GYsz5kDEPK6LSMjW+akmL8cdKkSZg0aVK66wiCgHfv3sHV1dVkMd/06BtztUS9SB1T1AtFhQsXVvvgTKRYzxLbMIb+Phl5/rLaPWXKWI0hsSJTiI6OlvLS9Ord1tbWKFq0KB48eKBTOWHKuIYxFO89daPBAMqjcmmrH+fLlw/Ozs749OmTxmW0XQvGtjEyKv6s7vmfLoxtA5m7Ddi1a1f8+++/uHLlCl68eAEvLy94eXkhd+7cqFmzJurUqYP69etr7WghEpfRNvq9Il2fn5qDtvq7SFPcSlP9WPytzNneTUxMxMWLFxEUFKSUp6c95xld//tSsbOFmejbm//169dah/Jt06YN5s6dCyB1eMmDBw9i3bp1OHnyJD5+/Ijk5GTcunULt27dwsqVK1GyZElMmzZNZRjFc+fOYcGCBbh3757KPtzc3FC9enXcvXs33SGPxaGRtVE3zJGu0uvkYG9vj6ioKK0Fs0iXZTStZ+rOFukNH6t4XsWetuJwd9bW1um+da64vqbCp0aNGkr/V+zdq422c6HYoNflfIvHpMtwuorLaCtQ0xo/fjxq1qyJLVu2wN/fH4mJiVLA8fz585gzZw7q1auH6dOn4+uvv9Zpm4pDD+rzW+qTbl2ld20qvkEuptsU6a9YsSKOHj2KtWvX4tixYwgLC4MgCNLQpRs2bECBAgXw+++/K71Vkx5Nwzpqo+laM2R0Bn2uSfHcaPpdM3p0iO+++04aktLKykrjqAtpCYKA+vXr48aNG/jw4QOmTJkCHx8fg4bzzEjaAiKamDLPSa/8U7z34uLipOvBHHmSvozNx/VdX3EYOk2jWpiL+HZRer+XtmMKCwvDvHnzcPToUZVe3jY2NqhUqRJy5syJ06dPG5RGc29fG/H8WFtba22Majo/pqybmOvaiImJwYIFC7Bv3z6Vt0nEgGjhwoVx9OhRg/dhSH5kKg0bNsSWLVukBnOhQoWkjhfiA8eaNWtiz549uHHjhtT5S7yeGjRoYFRdXR1ra2uTb1OxfpBe+aTtWmvbti1OnDiBp0+f4vbt2yhfvjyA/94G/v7775WGvjekXgIYVzcxpG6njSnrNoYwtm4n5lPp5RHG1Ftu3bqFv//+W+3bas7OzqhduzZCQkLUtp3FMi6jyzddJCYmYsWKFdi+fbvKkLAymQwlSpRAhQoVlIZGt5TixYsjd+7c0lRQunQYBlLrsXny5EGZMmVw9uxZ+Pr6olmzZnq1PyxF37LDlG1Qfeqxim/+mqsNaErmCq6bujwwlKenJ8qWLYu7d+/i0KFDUmeLgIAA6f4Rpw9QlBH1IWPpW0aLTBXfNCVj4o8iY2K+utI35mqJepE6pqoXAobFZwHDfp+MPn9Z6Z4yZazGUm0zxZckTF3vNkVcwxQUj1HTvsQXbaytrdN9Lubo6Kg1v9RWphvbxjBl/Nkco3kZ2wYydxvQ3t4emzZtwu7du7Fv3z7cuXMHQOpUcz4+PvDx8YGNjQ06d+6McePG6RRv0vU30ef5qanp0ubTNxai629t6P29adMmrF+/XmnKXVHhwoVRq1YtaQokMg12tsiiihcvjjlz5mDGjBm4efMmAgICEBAQgOvXryMxMRGPHj1C3759sW/fPqkn25kzZzBw4ECkpKTA3t4eDRs2RIUKFVC8eHEUL14cBQoUAAB06dIlQxsj6qQ3pJdYCGjrtSpSfAB0/fp1nTqKmEt6Q0cpVl7Eoc/E9CYlJUmBck0UCyd1GfXnz58xbtw4pc+mTJmCihUrIk+ePFrTFh8fr7GgViyUdflNxGPSZRhpxWPS97erW7cu6tati5iYGFy6dAmBgYHw9/fHgwcPAKTeE71798bhw4d1Cs4o7j+9tKf3WxhL13sE+K+nranSnz9/fkyZMgWTJ0/G3bt3ERAQgMDAQGlIwTdv3mDYsGHYsGEDatasqdPx2NvbIzo6Gk2bNsXixYt1WseUHB0d8fHjR72uycwQXI+IiMC0adOk/yclJWHs2LHYv3+/TlMDzZs3D76+vhg3bhzevn2LOXPmYNasWWZOtWbmGG4fMG2ek14axXtPJpOpVIhNnSfpy9h8XN/1NQXsM4L4tkV6jUbFIfwUxcTEoFu3btJbD99++y1q166NkiVLolixYvjmm29ga2uLPXv2GNQZwtzbT494fpKSkpCQkKDxetN0fkxZNzGH5ORk9O/fX5oWzsPDA/Xq1YOHhwe++eYblChRAvb29rh48aJFHy4Yo1q1anBxccHHjx9x/vx5dOnSRRpyVCx7a9asCZlMhoSEBFy7dg3ly5eXzom2IEVmopgPR0dHa30jW9u1VrduXeTNmxehoaE4fPgwypcvj5cvX0rD5KcdSlmxDO3bt69K/dkcDKnbaWPpuo2xdTtXV1eEhoamm49rm5pSmwcPHqBHjx6Ij4+HlZUVfvjhB1SqVAklS5bEN998g0KFCkEmk2HMmDFqHzqI58pcdZf0aNvvhAkTpDfdCxYsiIYNG6JUqVJS/pcjRw48f/48U3S2mD17tlIAcPPmzahfv366bYj8+fNj48aNyJUrF3766SeEh4dLD7YycpQ5Reaq65iyDapPPpO2HmuONmBmo+78WKI80KRdu3a4e/cuzp8/j4iICLi5uUkPT4sWLaoynHlWqQ/pW0YDlo9vasqDFe87X19fnd4sVseQmK8+9I25Zpb7wFT1QkD3dj2gmh/q+/tk9PnLSvdURsWHzUmfchrQr95tbFzDVBQfImt6S1+8ztOLMQDG1Z+NbWNYOv6cHmPbQBnRBrS2tkbXrl3RtWtXvH//Hv7+/lJ8882bN0hMTMSWLVvw6dMnzJs3T+N2xGvJkiOjWZI57+8FCxZg9erVAFJftG7UqBHKlCkj1f9cXV2RlJTEzhYmlj2v5EyoYMGC0lsB6v5p6pVlbW2NKlWqYPDgwdLbsuJUGfHx8di5c6e07Ny5c5GSkgInJyd4e3tjwYIF6N27N+rUqSNVmoDUh3eW9uLFC43fhYaGSj0K3d3d092W4jJBQUFalzX3kDmKQ3GqI85RZWdnJzUaFKeKUJy7XZ1Hjx5Jf6ubYsLLy0taZvTo0bCxsUFERIROQxtq+02ePHki/a3LbyKmLSwsTOr5qomYXplMZvDb3k5OTqhfvz4mTJiAgwcP4uTJk/j+++8BpA4fpuuDLMVzmt58Yoq/lS7nRF+RkZEqQ34qUvebmDr9MpkM5cqVw6+//oq1a9ciICAA48aNg0wmQ0pKCjZt2qTTsQCQ8iDxobM25rhPxXOT3j2muIw5fld9TZ06FaGhobC2tsaYMWMApP72f/31V7rrTpgwAdbW1mjdurU0fcjevXtx7tw5s6RVrDxrmwcuvfzAUKbMc3TNxwsWLKhxigNT5Un6MjYf13d9Z2dnqbe92KlAk+3bt2PVqlXSFAfGEn+7sLAwpTl009J0TNu2bZPSPHLkSOzevRvDhg1Ds2bNUKpUKSlwYGidydzbT4/ita3426Wl6fyYsm5iDsePH5ceLHTq1AmHDh3CmDFj0KpVK5QrV04KBGWGOq+hrK2tpXl7L1y4gIiICKmeKz7kcnNzk6aKCwgIwIULF5CYmAgnJ6cs8yBMn7qL4rWWNh+zsrKS5rs/duwYBEGQ5rF2dnZWeRPbxcVFCqyn134wlZcvX2qt4yjeq7rcS5au2xhbtxPzKW15FKC9bNJm/vz5iI+Ph1wux+bNm7FixQr069cPP/zwAwoXLiwFlTXVG8T0KQ4Zr86FCxewePFi7Nu3T+eOIcbUmW7evCl1tKhXrx6OHTuG33//HW3btkXFihWlcjkz5H8nT57E3r17AaTOK1y4cGEIgoAJEyZobeeIyxcrVgyurq5SWzYsLAwzZ840S1rF30TbHPXmrscCxrfhPn36pLVepLi+pqmKTNkGzCiKQXxN95UgCGqvO0uUB5q0aNECtra2SEpKgp+fH+Lj4+Hn5wdA/agWWaU+pG8ZDZg3vmlMHqy47/TKP13iGvrEfPWhb8w1s9wHpqoXAqlTdGj7DXTJD3X9fTL6/GW2e0qbjI4Pm4Ozs7M0HUJ69e7ExESp7qhLvdvYuIapKI6ioKmjhOJ98vTpU43bioiI0Hos6TG2jWHp+HN6jG0DZXQb8KuvvkLr1q0xe/ZsnDp1Cjt27JDO8aFDh7TmGWInA11G4QAMf36aWYm/9dOnT7Vea/re3yEhIVi3bh0AoGzZsjh+/DimTZuGjh07omrVqlInD0vX/75E7GyRxRw4cABt27ZFjRo11A5h5OzsjPHjx0uFoDj/zocPH6RMunbt2hrn+nn9+rVU6Ftyrh5tD1vExiSQOl91eqpXry79Lfb6V0cQBLRu3Rq1a9fGL7/8Ypbjf/jwId6/f6/2u4SEBPzvf/8DAFSqVEl60KKY/iNHjmjctiAIOHbsGIDUnnFp50fz9/fH5s2bAQCtW7dG//79MXDgQADA6dOn0+3JpstvYmVlJT201aZatWrS39reoPj8+bN0TkqXLq3UW1jb0Exz5szBTz/9hK5du6r9vlChQtKDaUD3ucE8PDykAknbb5H2e13ngNOXtgey4m/i6uoqzclubPrPnTuHTp06oVq1amobtvb29ujbty9KliwJACrDVGn7zcTpbZ49e4Zbt25pXO727duoUKECfvzxR2nqDFMQr8mHDx9qrZDevn1b6sVvrt9VV3v37pV+5/79+6Nfv35SkG3btm04c+aMztuaPn26dH9NmTLF4GEutRF7SmtrQItvWpiaKfIc0dWrVzX2Kg4PD5eGIlfMu82VJ+nL2Hxc3/VlMhmqVKkCILUM0vSQSRAELF26FAsXLjTZW7b16tWTtn3ixAmNy2m6TxSvxR49emhcX5y2AVCd+1VbnmeK7RtDPD9AaiBek7Nnz6r93FR1E3O5evWq9Hf37t01/hbmOr8ZpUGDBgAgvU0nCAKKFSum9Jaf2JHL399fuofr1Kmj1+g5pp4aRB+VK1eWOq6lV3cR83crKyup7qOoXbt2AFLrJzdv3oSvry+A1Dnu047OIpPJpKGfAwMDtebLR48eRcWKFdG0aVPpejfEx48fla7dtMR8tlSpUmqHfk7LFHWb9H57c9btxHzqwYMHGjs0JCcn4/z581rTqIl4rkuXLq1xGPaYmBhcv34dgGrbWFwnIiJC6/Ht2rULK1aswF9//SUNNZzeeVWsM2lqk2qqMyleQ126dNE4vLGl87+wsDBMmTIFQGpwfty4cZg5cyZkMhnevXunNHJbepo3by7FBXx8fJTiBaYi1glTUlI01mXNVY81dRtU2z0j5mGOjo7SsPPGtgGNZYoySLFOrymwfP/+fbVvFlqiPNAkZ86c0shUJ06cwKlTpxATEwO5XI6ff/5ZZfmsVB/Sp4w2d3zTmDy4atWqUr1FW/wRAAYOHIjvvvsO7du3l9rfhsZ89aVvzDWz3AemrBd+/PgRt2/f1ri+2EYqVKiQ9EDM0N/HEucvM91T2pgyVmNJ4nH4+/trjXn9+++/UjxJl5iisXENU1Hs3KKpnBensgS0x6vF39FQxrYxLB1/To+xbSBztgE/f/6MX3/9FQ0aNICXl5faZSpXroyePXsCSK1TaHoOBvx3LSl26MpOxN86KioKAQEBGpfT9/6+ceMGkpOTAaR2xtXUmUWx/mfJ58BfEna2yGKcnZ1x9+5dREZGaszonz9/Lg0VVKRIEQBQerv26dOnahtQsbGxmDBhgnRzaevFbW4HDx7EzZs3VT4PCQnB8uXLAaT2zCpdunS62ypbtiy+/fZbAMCePXvg7++vdrktW7YgKCgIoaGhSm8zmVJKSgpmzpypNgPz8vJCWFgYACg9kCtVqpQ0HOTmzZs1VgTWrl2Lhw8fAgA6dOig9PZGVFSU9NvmzZsXv//+OwBgwIAB8PT0BADMmzdPa8/Tf/75R+1QcQ8ePMDWrVsBpA5Jrcs0Ij/88INUkC5evFjjEHSzZ8+WKqkdO3ZU+k5xLj5112pwcDCuXbsmvcmRluJwwOJ9kh65XI5OnToBSJ3jWdNbO1euXMHu3bsBpA4NX6pUKZ22r6/FixerzMMMpAbQxEZZmzZtpHNlbPrz5s2LGzdu4OPHj9iwYYPadSMjI/HmzRsAqudVTIe6B66dO3eW7rmpU6eqbbjGx8dj1qxZSEhIwPPnz1GhQgW1aTBEp06dpP1PnjxZ7bCaMTExUuDXyspKarxawsuXLzF79mwAqQHY3377DUDqaBV58+YFAEyaNEnnXqru7u4YMWIEAODt27dm6Q0s9nIPCAhQmy4fHx+zveVhijxHFBkZiUWLFql8npKSgunTp0v5UdqOFebIk/RlbD5uyPrieYyKilJ73oDUeQTFa6Jly5a6H5AWVatWla65xYsXq23gXb58WWNAR7HepOmtqW3btiEwMFD6f9qySFs5ZYrtG8Pd3V16CL9x40alN79ET548kTpppmWKuok5KZ5fTQGGU6dO4cCBA9L/LVnvNVTdunVhY2ODT58+SW8upB2xQvz/nTt3pMB6o0aN9NpPenUuc8qVKxeaNWsGIPUh4MmTJ9Uu5+vrKwX1mjRpIr1dpqh48eJSm2Dt2rXStaHubWDgv3w8MTERkyZNUlt/+fDhAxYtWoS4uDi8fv1a2r6h5s2bp7ZD3549e6T7rH379jptyxR1G211t/S+N7Zu17p1a9ja2kIQBKXyVdGaNWvw9u1btWlLj5j2169fq31DLykpCVOnTpXa1YmJiUptuJ9//lnqtDR37ly15+DWrVtSQLlly5bS+UjvnhLLr5iYGLXBtWfPnmHfvn1qj0uX/O/27dtSnqEpDeYm1lPlcjlmz54Ne3t71KhRQ6o3HDlyBIcOHdJ5e9OmTZMCidOmTVPbRjKGYt1M3QO+iIgIje0jY5m6Dbpo0SK1b5SePHlSymPbtm0rPYwztg1oLMX7xdBpgxTTJD5wVJScnKyxngpYpjzQRCyzAgMDsX//fgCpHSvVTaeQlepD+pTR5o5vGpMHu7m5oUmTJgBSOy2Lv1FaJ0+exOnTp/HhwwfkypVLejhvaMxXX4bEXDPDfWDKeiGQ2v5XdxybN2+W2ueK7Xpjfp+MPn+WuKfEDp763G+mjNVYUpcuXQCk1m3/+OMP6UGnorCwMGkUWkdHR7Ro0SLd7Rob1zCVokWLSuWxpg4AxYsXl17KWL9+PV69eqWyTHh4OJYuXWpUWoxtY1g6/pweY9tA5mwD2tnZITw8HK9fv8b+/fsRHh6uNg13796VtqNpREZBEKQRGzR18PrSNWrUSOpQPXv2bLUvPh4+fFjq/K8rXep/L1++xPz586X/Z8V4WGbEzhZZTP369aWH40uXLsWMGTNw584dRERE4OXLlzh8+DD69u0LQRBga2uLzp07A0gdskwMij98+BAjR47EnTt38OHDBzx79gx79uxBmzZtpDdyAd3mGTOXxMRE/PLLL9i8eTPevXuHsLAwHDp0CB07dkRYWBisra31GiJ0+vTpsLe3R1JSEvr164eFCxfi0aNH+PDhA+7fv4+ZM2dKDy7z5s2LoUOHqmyjadOmaNq0KXr16mXwcVlZWeH48eMYMGAArl+/jg8fPuDevXsYPXo0Nm7cCCD1NxYbZ6Jp06bB3t4eCQkJ6NWrF1atWoVnz54hKioKt27dwsSJE6UehaVKlVJJ/7Rp06Se09OmTZMychsbG8yePRtWVlaIi4vD2LFjNQ6N+vHjR3Tp0gX79+9HaGgoQkJCsG3bNvTs2RPx8fHIkSOHTtORAKmF7Z9//gm5XI7IyEh07NgR27Ztw6tXr/DhwwdcuXIFAwcOlIbcq1OnjnQti8RjAFJ7l4eHh0tBtZ49e8LR0RGCIGDQoEHYsGEDHj9+jA8fPiA4OBhr167FrFmzAADffPMNateurVO6gdQRBL755hsAqQXh5MmTcefOHURFReHp06dYvnw5+vbtKw3TrW1uMmO9fv0anTp1gp+fn5QHrF69GoMGDYIgCHB3d1e5FoxJf6lSpfDDDz8ASB1VYfTo0bh27RrCwsLw5s0b/Pvvv+jVqxeio6Mhk8lU3tYWf7OwsDD8+++/+PDhg5TPlChRAv379weQ+lZR+/btcejQIYSEhOD9+/c4c+YMevbsKVUw2rVrZ9KGe/HixTFgwAAAwPXr19GxY0f4+fkhLCwMYWFhOH78ODp16oQ7d+4AAIYPHy4NzZ7RkpOTMXbsWMTExMDKygpz5syRgv05c+aUKsyhoaGYOnWqztvt0aOHUsc0Q98U1eTHH38EkDrkYJ8+feDv74+IiAg8fPgQ8+bNw7hx4zQGQYxlijxHZGVlhQ0bNmD8+PG4d+8ePnz4gGvXrqFfv37S2y/du3dHuXLlpHXMmSfpw9h83JD1GzZsKOUb69evx9ixY3Hr1i18+PABDx48wLx58/D3338DSB0tQd2QpoaQy+WYNWsW5HI53r9/j06dOsHHxwfh4eF48+YNNm7cKOU56ojTMwCp03ycOHECISEhCAkJwYULFzB8+HD8+eefSuukfRtSWzlliu0ba9q0aXBwcEBsbCy6d++OHTt2SHnu3r170b17d61zQxpbNzEnxfP7559/Yv/+/Xj9+jXCwsJw5coVTJkyBYMGDVIKJJr6/GYEZ2dn6S05MaCRtrNFtWrVYGNjg+TkZERGRsLGxkZpZBNduLq6SgGbEydO4O3bt1IH4Ywwbtw45M6dG4IgYPjw4fj777/x8OFDREVFScOFjh07FgCQL18+6W15dcQAsxicL1asmNq3HYHU60gMgp4/fx6dO3fGyZMnER4ejrdv3+LIkSPo2rWrNCXQwIEDjR7S+NatW+jWrRsuXrwovWX4119/SeV5+fLlNY6SlJYp6jba6m7pfW9s3a5QoUIYMmQIgNS3bnr16oXAwEBERkbi0aNH+PPPP7Fo0SKNU3alR8wnIiMjMXDgQFy5ckWqT/v4+EjlhiLFfCJPnjwYPnw4gNS3x7t3744zZ84gIiICz58/x7Zt29C/f38kJibCzc1N6hireN6A1AdC79+/V+qIKtaZAGD8+PE4cOAA3r9/j5cvX2Lr1q1SEFXxIbSodu3aUse2ZcuWYfPmzXjx4oU0Ase8efPQtWtXpQ4mGZ3/7dixQ3oI1qNHD2kULCD1fhfvoz///FN6gJ+e/PnzY/To0QBSr8cZM2aYNM0NGjSQHiLNmzcPW7Zswdu3b/H27VscOHAA7du3R3h4uNnetDVVG1Qul+P58+fo1KmTlJ+9fPkSy5YtkzpeFyhQAMOGDZPWMbYNaCzF+2Xv3r2IiIhQ+2BEG09PT+lh1fHjxzF9+nQEBwcjPDwc586dQ8+ePXHmzBmNbRFLlAea1K5dG/ny5UNiYqLUEUBTp8GsVh/StYw2d3zTmDxYXCd37twAgIkTJ2LatGnSA/rHjx9jyZIlGDlyJIDUB64TJkyQ1jU05qsvQ2KumeU+MFW90MrKCtevX0ePHj1w8eJFREREIDg4GLNmzZLiw2XLlkX37t2ldYz5fSxx/jL6nhLz66CgIKlepa09CZg2VmNJtWvXln7f48ePo1evXjh//jwiIiIQEhICb29vtG/fXpqWdsaMGTq9rGhsXMNU7O3tUbZsWQCQ2g7qTJo0CTY2NoiKikLnzp2xb98+hISEICwsDL6+vujYsaPSyC6GvOhqbBvD0vHn9BjbBjJ3G1DcdmhoKHr06AFfX1+8fPkSERERuHPnDqZOnSpNW9SuXTulKWgUvXjxQqrPaRpl8Evn7OwslVEPHz5Ely5dcOrUKak9uXjxYkyYMEHv9m7VqlWlNsmuXbuwdOlSPHnyRIrDLl++HG3atFGK6WTFeFhmpL5mSJmWXC7HkiVL8Msvv+D169fYunWr9DapIkdHR3h5eaFQoULSZ9OmTUOPHj3w8eNHHDt2TO1wZCVLlkSZMmVw8OBBREVFSb2sM1qLFi1w/PhxzJo1S3oAJXJxccGiRYtQpkwZnbdXunRprF27FsOHD0dERARWrVqFVatWqSzn7u6OlStXSg0jReKoD4a+TQGkPnSKjo7GmTNn1PaQr1evntphmEqVKqWU/oULF2LhwoUqy9WqVQvz5s1TGhLu0KFD0ts3LVq0UHmbsVy5cvjll1+wdu1a3L59G8uXL5cCh4p+/vlnHDx4UKkhKMqfPz9Wrlyp05DGojp16mDBggX4/fffERERofJQSdSiRQtMnz5dpQJWvnx5ODs749OnT9Lv+d1332HTpk1wd3fH/PnzMWrUKERFRWHevHlqA06FChXC6tWr9Sq0cuTIgQ0bNmDw4MG4e/cu9uzZgz179qjd9oIFC8w6ZHrbtm3h7e0tVcIUlSxZEitXrlQZKsrY9M+ePRt9+/bF/fv34ePjoxKEBlI78UyZMkUpcAqkvnGzZs0a6YEzAAwbNgyDBw8GAIwYMQIpKSlYt24dnj17JjWQ02rRooVeQwvravjw4UhOTsa6desQFBSk9rza2Nhg2LBh6Nevn8n3r6s1a9ZIlf5ff/1V6aE+kNoz9qeffsKRI0fg5+eHvXv36vQWrFwux8yZM9G2bVskJiZi8uTJ8PHx0Vgx1lfPnj1x+vRpXL9+HQ8ePEDv3r2Vvi9atCjGjh0rXQ+mZmyeI+rQoQOuX7+OAwcOKL0FJmrXrh0mTpyo9Jk58yR9GJuPG7K+TCaDl5cXRo0ahTNnzuDQoUNq31StXLkyli1bZtJRpapWrYolS5ZgzJgxePPmjfQQRmRvb48xY8ao/S3atWuHEydO4Pz583j16pXajgL29vb49ddfsWzZMgCQAmMibeWUKbZvrCJFimD9+vUYPHgwPnz4gGnTpinlrVZWVpg4caLGzq3G1E3MrXbt2lIZ+eHDB7XXrJWVFQYOHIiNGzciPj7e5Oc3ozRo0EAatU0ulytN8QKktgm+/fZbaVSdatWqwcXFRa992NnZoUqVKrhy5QrOnz+PH374Aba2trh+/brGBw2mlC9fPmzcuBGDBw/GixcvsG7dOqW38kVly5bFggULtAYumzdvjjlz5khv+Gh6QCWaPXs2rK2tceDAAdy9e1dtGSWTydCnTx+pbmMoGxsbNGnSBD4+PtJ834qqVauGJUuW6FVGGFu3Sa/uZu66Xf/+/REdHY01a9bg6tWr0pC0ooIFC+LHH380aESBsWPH4tq1a3jz5o00FU9aX3/9NRo0aIBt27YBSM2HxUAzkFoPi46OxqpVq3Dz5k21we58+fJh1apVSu3LIkWKwN3dHa9fv4a3tze8vb1RqFAh6WFI5cqV0aNHD2zZsgWRkZEYP3680jZz5MiB5cuXY9CgQSod5YsXL47Bgwdj6dKl0lt5advTQOpbof7+/nj58qXW0Q1N7enTp1K5W7hwYemBoyhHjhz4888/0a9fP3z69Anjx4/Hpk2bdBoZqWvXrtLbXz4+PmjWrJneI/lo4u7ujtGjR2Pu3Ln4/PkzZs6cqVQ+ii8xrF27Vu1oUcYyVRvUzc0NnTp1wvLly9XmZ0WKFMGaNWtUOh0Y0wY0VvXq1WFtbY2kpCRMnz4d06dPR7t27aSHobqaNWsW+vXrh9jYWGzfvh3bt29X+r5r166QyWTS/Z5WRpYH2ohThqxevRpAanxM03We1epD+pTR5oxvGpMHA6ntIsV6y44dO9RO2+vq6oolS5YovdFrTMxXH4bGXDPDfWCqemG5cuXwzTffYP/+/WrrXeXLl8eqVauUpt4z9vfJ6POX0ffU999/D19fX8TGxqJbt24AUjsoqpvmSJGpYjWWpvj7Xr58GZcvX1ZZxtHREVOnTtVpVAuRMXENU6pbty5u3ryJGzduICEhQe20lKVKlcLChQsxevRohIaGSiNrK+revbt03xga+zK2jWHp+HN6jG0DmbMN2LRpUwwYMACrV69GcHAwRo0apTYNderUUfv7i8RRXK2srMz2wllW0KJFC0RFRWHWrFl4+PAhBg4cqPS9q6srevbsiSVLlui8TVdXV0yZMgWTJk1CSkoKli1bJsUVFTVs2BDh4eG4ceNGhrYHv2Qc2SILKlq0KA4dOoQxY8agSpUqcHV1hZWVFVxcXFCmTBkMGDAAx48fl+ZxFpUqVQoHDx5E165dUaRIEdja2sLGxgZ58+ZFrVq1MHPmTHh7e0u9dgVBSHcOPHNp0KABvL290bRpU7i5ucHBwQEeHh4YOHAgfH19UatWLb23Wb16dfj5+WH06NGoXLmy0nmrUqUKJkyYAB8fH6mXsjlYW1tj7dq1mDBhAjw8PGBnZ4ecOXPiu+++g5eXF1avXq1xHqXq1avjxIkTGDVqFCpVqoScOXPCxsYG7u7u+PHHH7Fy5UqsX79emkIAAN68eSNVUnPnzo3Jkyer3fbQoUOlgMzq1avVDk/UpUsXbN26FfXq1YOLiwucnJxQtmxZjBkzBocPH9ar84uoWbNmOHHiBAYOHIgyZcogR44csLOzQ5EiRdCqVSts3rwZXl5eah/2urq6Yu3atahWrRocHR1hb2+v1AuvYcOGUrDa09MTTk5OsLa2hpubG6pXr47ff/8dvr6+KFy4sN7p/vrrr7Fnzx7MmzcPdevWRZ48eaR7qXr16pg+fToOHjxo9mHGRo0ahZUrV6J69epwcnJCzpw5UalSJUybNg179+7V2PA2Jv25c+fG7t27MW3aNNSsWRNubm6wtraGk5MTSpQogZ49e0pvA6ZVs2ZNzJ49GyVLloSdnR2cnZ0RFRUlfS+XyzFmzBgcOHAAnTp1QrFixeDo6AgbGxvkz58fTZs2xbp16+Dl5aXXXPO6Eve/f/9+dOjQAUWKFIG9vT1y5MgBDw8P/PLLLzh06BD69+9vscbdnTt3pGE9S5QoobbCDABTpkyRGr2zZs3SOBRjWh4eHlJF++3btyZtrDk6OmLz5s2YPHkyvv32Wzg5OcHR0RGenp4YMWIEDhw4oHb4W1MyJs8ROTs7Y9euXRg0aBCKFi0KW1tb5MqVC/Xr18fatWulBnZa5syTdGVsPm7o+jly5MCaNWuwfPlyNGzYEHnz5oWNjQ1cXFxQvXp1zJw5E1u3bjXLyCaNGzeGj48PevTogWLFisHOzg558uTBTz/9hH379inNEavI2toaq1evxpQpU1CxYkU4OTlJdYayZcuib9++8PHxwdChQ6UAadoHD9rKKVNs3xSqVKkCHx8fDBw4EB4eHnBwcICrqyvq16+PrVu3onnz5lrXN6RuklHmzJmDv/76CzVq1ICLiwusrKyk/Lxr1644cOAARo4cKXVOOH78uFEdai1FnLsdSO1crPgGsEhxtAtDHzwuXLgQzZo1g6urK2xsbODm5mbw9A2G8PDwgI+PD6ZMmYIaNWogV65cUv2gTp06mD9/Pnbu3Cm9uayJs7OzdA7kcjlat26tdXk7OzvMmzcP27ZtQ+vWrVGwYEHY29vD1tYWBQsWRJs2bbBz506MHz/e6LqB2Dltzpw5qFChAhwcHODm5oYaNWpg/vz52Lhxo05vwCkytm6TXt3N3HU7mUyG0aNHY/v27WjSpAkKFCggnfvevXtj//79yJMnj17nRJQ/f354e3vj119/RYkSJWBnZwcbGxvkzp0b1apVw8SJE+Hj44MBAwZIgWB1+fCIESPg7e2Ndu3aoWDBgrCzs4ODgwNKly6NoUOHqi0frayssHbtWtStWxcuLi7SUMGKb39OnjwZK1euRL169ZArVy7puMUOBWlHsVE0ZMgQrFmzRlrXysoKjo6OKFasmHTNzpgxQwpsiqONmVtSUhLGjRuHuLg4yGQyzJ49Gw4ODirL1a1bF23atAEAXLp0CevXr9dp+zKZDLNmzZJGoDD1dCJ9+vTBtm3b0KRJE6kukz9/frRp0wbe3t7p5ifGMlUbdNiwYVi+fDmqV6+OHDlywMHBAWXKlMHo0aNx8OBBtfmoMW1AY33zzTdYtmwZypUrBwcHBzg6OqodZjk9VatWhY+PD7p27YpChQpJdfi6deti9erV+OOPP7Sun5HlQXoUH5g2a9ZMa4fWrFQf0qeMNnd805g8GPiv3jJ16lTUrFkTuXPnlu6ZcuXKYejQoTh27Bhq1Kihsq6hMV99GBpzzSz3ganqhXPnzsXs2bNRvnx5ODo6wsnJCZUqVcIff/yBnTt3qq1jGPP7ZPT5y+h7ql27dhgzZgyKFCkCGxsbuLq66ly/MEWsxtLE33fLli1o2bIl3N3dpbh/2bJlMXToUBw9elSq4+jD0LiGKYlTvsbGxiqNcqIprZ06dULBggVha2uLnDlzom7duiojcdjb2xuUFmPbGJaOP6fH2DaQuduAo0aNkvKxwoULw97eXjp3P/74I5YuXYp169Zp/X3Fl5C///57g9tzX4pu3bph//79aNu2LQoXLgxbW1vky5cPHTp0wMGDBw2aZqVt27ZK7RZra2vY29ujUKFCaNq0KdauXYsVK1ZIZcTDhw+lKYDJcDJBceJRIgsKDAyUeuotWLAg3QA/mZ+3t7f0hvauXbs0DrNMGWfp0qVSb8Tz589b5AEWEWUdxubjLAeIiMxrwoQJ2L9/P2xtbXH79m1LJ4eIiIi+QIy5EpGx+vfvjzNnzqBNmzaYO3euQdt4/PixlP/89ddfZu+sSplPVFQUateujYSEBKmDONGXgCNbEBEREREREREREREREZEKcfrT48ePK410AAAREREYNmwYFixYgODgYI3buHXrlvS3IW/sU9Z34MABJCQkoGLFiuxoQV8UdrYgIiIiIiIiIiIiIiIiIhXly5dH/fr1ERsbi127dil95+zsjLNnz2L16tVYt26d2vVjYmKwYcMGAECePHlQunRps6eZMpfk5GRs2bIFwH+dd4i+FKqTihMREREhdW5txfnDDWFnZwdra1Y39BUfH4/k5GSjtuHk5GSi1GSc5ORkxMfHG7UNGxsbi8yrmREEQUBsbKxR2/iSzw8REZGimJgYo9a3srIyeD7x7IxtiFQJCQlITEw0ahv29vawsrIyUYqIiIiMM2bMGFy4cAEbNmxA165dkSNHDgCpcYbmzZtj79698Pb2ho2NDTp06AB3d3fExMTg3r17WLVqFR49egQAmDhxIsu3bGj//v14+fIl6tSpg9q1a1s6OUQmlbVbLkRERGQ2hw4dwsSJE43axpw5c9C2bVsTpSj76NevHy5dumTUNoKCgkyUmoxz5coVaS5hQw0ZMuSL7SH/+vVrNGzY0KhtGDO/KhERUVZSuXJlo9avXr269PYd6Y5tiFSrV6/GsmXLjNrG5s2bUaNGDROliIiIyDglSpTA6NGjMWfOHKxbtw4jRoyQvhs/fjyCg4Nx/fp17Nq1S2X0CyC1U8bo0aPRokWLDEw1ZQZxcXFYtmwZXF1dMWvWLEsnh8jk2NmCiIiIiIiIiIiIiIiIiDTq1asXTp8+jfXr1+Pnn39G0aJFAQAuLi7Yvn07Dh06BB8fH9y/fx+RkZFwcnJCvnz5UKtWLXTo0AHFixe37AGQRaxcuRJv377FokWLkC9fPksnh8jkZIIgCJZOBBEREREREREREREREREREVFWIbd0AoiIiIiIiIiIiIiIiIiIiIiyEna2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6YGdLYiIiIiIiIiIiIiIiIiIiIj0wM4WRERERERERERERERERERERHpgZwsiIiIiIiIiIiIiIiIiIiIiPbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFERERERERERERERERERESkB3a2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6YGdLYiIiIiIiIiIiIiIiIiIiIj0wM4WRERERERERERERERERERERHpgZwsiIiIiIiIiIiIiIiIiIiIiPbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFERERERERERERERERERESkB3a2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6YGdLYiIiIiIiIiIiIiIiIiIiIj0wM4WRERERERERERERERERERERHpgZwsiIiIiIiIiIiIiIiIiIiIiPbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFERERERERERERERERERESkB3a2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6YGdLYiIiIiIiIiIiIiIiIiIiIj0wM4WRERERERERERERERERERERHpgZwsiIiIiIiIiIiIiIiIiIiIiPbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFERERERERERERERERERESkB3a2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6YGdLYiIiIiIiIiIiIiIiIiIiIj0wM4WRERElKV4e3vD09MTbdu2tXRSiIiIiIiIiMhM2P63jIcPH8LT0xOenp6WTgoREVGmx84WRERERERERERERERERERERHpgZwsiIiIiIiIiIiIiIiIiIiIiPbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFEREQWExQUhHLlysHT0xNr1qxR+f7AgQPw9PRE+fLlERQUpHVbycnJGDp0KDw9PVGvXj28fPnSqLQ1aNAAnp6eiIiIwIkTJ9C1a1dUrlwZlSpVQufOneHr66uyztKlS+Hp6YmNGzfi+fPnGD16NGrWrIlvv/0Wbdq0gY+PDwAgMTERq1evRpMmTVCuXDnUrl0b06dPR3R0tM7p8/T01Pmft7e3ztuNjo7GmjVr0KZNG1SuXBkVK1ZEu3btsH37dqSkpOi8HSIiIiIiIiIR2/+Zr/0fGhqK2bNno2HDhqhQoQKaNWuGzZs3s+1PRESkB2tLJ4CIiIiyL09PTwwZMgQLFy7E8uXL0axZMxQqVAgA8O7dO8yaNQsAMGrUKHh6emrcjiAImDRpEk6cOIGvvvoKmzdvlrZjrJUrV2Lz5s1wdHRE0aJF8ebNG1y/fh3Xr19HaGgoevfurbLO3bt3sXjxYiQlJaF48eJ49+4d7t27h9GjRyMpKQmHDh3ChQsX4O7ujiJFiuDx48fYvn07nj59io0bN+qUrsqVK+t8DLlz59ZpudevX6Nfv34IDg6GlZUVihcvjsTERNy5cwd37tzBzZs3MW/ePJ33S0RERERERASw/Z/Z2v9Pnz5Fnz598PbtW9jb26NkyZIICQnBrFmzULVqVZ33R0RElN2xswURERFZVL9+/XDy5Encvn0b06dPx7p16yAIAiZOnIiPHz/iu+++UxvQUDRz5kzs378fefPmxaZNm1CkSBGTpW/z5s0YOHAgBg8eDFtbWyQkJGDChAnw9fXFsmXL0K1bN9jY2Citc+jQIVSpUgVLly5F7ty5kZCQgMGDB+Ps2bOYOHEiXFxcsHHjRtSsWRMAcPz4cQwbNgz+/v548OABSpUqlW66duzYYbJjFE2YMAHBwcGoWLEiFixYAHd3dwDApUuXMGDAABw4cAC1atVCq1atTL5vIiIiIiIi+rKx/Z852v/iOX/79i1q164NLy8vuLq6IiUlBRs3bsRff/1l0v0RERF9yTiNCBEREVmUlZUV5s2bB1tbW5w7dw5HjhzBjh07cPHiReTMmRPz5s2DTCbTuP6CBQuwdetW5M6dG5s2bcI333xj0vTVqVMHI0eOhK2tLQDA1tYW48aNAwB8+vQJwcHBao9p/vz50hsltra2UsAoJSUFI0aMkAItANCkSRMULlwYAPDgwQOTpl9X165dw6VLl+Do6IgVK1ZIHS0AoHr16hgyZAgA4ODBgxZJHxEREREREWVtbP9njvb/lStXcP36dbi4uGDhwoVwdXUFAMjlcvzyyy9o3bq1RdJFRESUFbGzBREREVlc8eLFMWzYMADA7NmzMX/+fADAtGnTkD9/fo3rrVmzBqtXr0aOHDmwadMmFC9e3ORpq1evnspn+fPnh4ODAwConWe1ePHiKFCggNJniv+vXbu2yjpiYCYmJsao9BrqzJkzAID69eurHXa0U6dO8PX1xcqVKzM6aURERERERPSFYPvf8u3/c+fOAUg9XhcXF5Xv27dvn9FJIiIiyrI4jQgRERFlCn379sW///6L69evAwBatmyJn376SePywcHB8PLyAgDExcUhLi7OLOnKly+f2s/t7OwQFxeHlJQUndZRHGo0V65cGr8XBEGndHXp0kWn5QBg4MCBaoNGil68eAEAKFmypNrvc+TIgRIlSui8TyIiIiIiIiJ12P63bPv/2bNnAKCxje/p6anz/oiIiLI7drYgIiKiTEEul6N27dpSsMXDw0Pr8vHx8XBxcUG5cuVw8eJFTJ48Gd7e3rC2Nm31Ju18rGmpC444OjpqXUfbsKi6unbtms7LhoeHp7tMZGQkgPTTTkRERERERGQMtv/1Y+r2vzhChzhiR1rOzs6QyWQ6dwYhIiLKztjZgoiIiDKF4OBgrFmzRgpELFu2DA0bNtQ4NKiTkxM2bNgAd3d3NG3aFEFBQVi3bh0GDhyYkcm2mKCgIJNuz97eHgDM9oYQEREREREREcD2v75M3f4Xpw6JjY1V+/3nz5/Z0YKIiEhHcksngIiIiCg5ORkTJ07E58+f0bFjR3To0AGfP3/GhAkTkJycrHadokWLonz58nBzc8OYMWMAACtWrMDTp08zMulfjKJFiwIAHj9+rPb78PBwtG/fHqNGjUJSUlIGpoyIiIiIiIi+FGz/W16xYsUAAPfv31f7fXBwcEYmh4iIKEtjZwsiIiKyuHXr1uHmzZvImzcvxowZg7FjxyJv3ry4desW1q1bl+76HTp0QKVKlfD582dMnTqVb2AYoE6dOgCA06dPS1OKKPLz88Pt27cRHBxs8qFaiYiIiIiIKHtg+9/yGjZsCAA4e/YsQkJCVL7fv39/RieJiIgoy2JnCyIiIrKohw8fYtmyZQCAKVOmwMXFBS4uLvj9998BAEuXLsWjR4+0bkMmk2H69OmwtrbGpUuXsHv3brOn+0vz/fff49tvv8WnT58wbNgwhIaGSt9dunQJXl5eAIA+ffpYKolERERERESUhbH9nzmUK1cO9evXR1xcHIYMGYJ3795J3+3Zswfbt2+3YOqIiIiyFna2ICIiIotJSkrChAkTkJCQgIYNG6JJkybSdz/99BPq1q2LxMREjB8/Pt2pKzw9PdGrVy8AwN9//43379+bNe1fooULF6Jw4cIIDAxE/fr10aZNGzRq1Ag9evTAx48f0b59e/z888+WTiYRERERERFlMWz/Zy4zZsyAh4cHbt26hUaNGqFt27b44YcfMHnyZNStW9fSySMiIsoy2NmCiIiILGbVqlW4e/cucuTIgT/++EPl+z/++AOOjo64e/cuVq9ene72hg4dCnd3d3z69Al//vmnOZL8RXN3d4e3tzcGDx6MIkWKIDg4GOHh4ahcuTLmz5+PWbNmWTqJRERERERElAWx/Z+55M2bF9u3b8fgwYPh7u6Ox48fQy6XY8iQIZg/f76lk0dERJRlyAROakZERERERERERERERERERESkM45sQURERERERERERERERERERKQHdrYgIiIiIiIiIiIiIiIiIiIi0oO1pRNAREREZA737t3DjBkz9F6vTJkymDJlihlSRERERERERESmxvY/ERERWQo7WxAREdEX6dOnT7h27Zre61lbs3pERERERERElFWw/U9ERESWIhMEQbB0IoiIiIiIiIiIiIiIiIiIiIiyCrmlE0BERERERERERERERERERESUlbCzBREREREREREREREREREREZEe2NmCiIiIiIiIiIiIiIiIiIiISA/sbEFERERERERERERERERERESkB3a2ICIiIiIiIiIiIiIiIiIiItIDO1sQERERERERERERERERERER6cHa0gkg9RISkhAVFWfpZOgsb15nAEBo6CcLp4SyEl43ZCheO2SIjL5u5s+fjX//9UO9evUgCALOnj2Lhg1/xJgxEzNk/2Q6zHPIELxuyFC8djIP8bfICBkZA+A1RpkZr0/KzHh9UmZmqeuTsQ9KD/NOysx4fVJahsQBOLIFERERkYndu3cH//7rB1tbW4wcORKjRo2Cra0t/v33BO7du2Pp5BERERERERERGYWxDyIiIo5sQUREREbq1q2dpZOQqQiCgI8fPwIAevTogYIFCwIAunfvjg0bNmDcuBFwcXGBTCazZDIzpW3b9lk6CUREREREREQZKivGVb702AfjE0REpCuObEFERERkQp8/f0ZycjLy5cuH3r17S5/36dMH+fLlQ3JyMj5//my5BBIRERERERERGYGxDyIiolTsbEFERERkIikpKYiLS51vfcSIEXBwcJC+c3BwwPDhwwEAcXFxSElJsUgaiYiIiIiIiIgMxdgHERHRfziNCBERESlxcrKCtbU1kpKSEBOTrNe629r0NFOqsoYV/qdwOPImqlSpgkaNGql837hxY+zduxfXrl1D3QJF8VvNHzI+kZlMt/2bLZ0EIiIiIiIiokwhK8RVvtTYh77xCWPiZ0RE9OXgyBZEREQEV1d75MmTA255HOHo6AhbW1s4OjrCLY8j8uTJAVdXe0snMdN7GhEK3/u3YGVlhbFjx6qdl1Qmk2Hs2LGQy+XwuX8TTyPCLJBSIiIiIiIiIiL9ZffYB+NnRESUFke2IKMlJSVi27ZtAIAGDZrB2tpGr3UPHz4IAGjZsrVe62Z1WfXYDU132vUA6HTd6LI/cZmkpEQEBd2HXC7HqFHjYW/voLKsocehuHyzZs1x9KgvUlJSeyzL5VbSMaWmIwEPHwZBJpNJ6RDXT0lJRkpKCh49CoKnZ2m0bt0WSUlJ8PKai7CwUFStWh2XLwcCAGrWrAVbW3ut6UubrsOH9+PixfPIkycvRo+eAGtra2m/iuRyK5XjUPxO8XhSUpKRlJQEf//zUroAGfz9zyE6OgaOjg6IiYlBcnIyihcvgZEjx+PoUR+cP38akZGRsLW1RaNGTdGuXccsc51nJzlz2kFuLYe13Bqfkj7hQPgB3I29i9jkWDhaOaKsY1n8nPtnONs4I1duGVKSUhAVxTk30xIEASv9TyNFENCpfXuUKFFC47IlS5ZE+/btsXv3bqwKOI25zdqpDU4QZXdJSYk4eNAbQUH34elZGk2a/ISFC+fh+fNnkMlkcHJyQrVq3yEw0B8REeEQBAH29g5wcnKS7qlcudzw7NkT2NjY4IcfGuHZsycQBAGlS5dF8+at4Ot7CEFB91GypAfkciul8hEAGjdugiVLvAAAw4aNhp/fcQCpdYf4+HhMmTIeKSkpyJMnD+RyK3h4eEIutwIAODs7oGPHjkp1AOC/ctba2kZt/UhTuZ1efSXt+Wrduq3K8rrWq9LbjjlYsm6cVevl+soux0lElB1khjzdXGkw57GljaGIdUFD6zzqYkXqtt2kyU9YvHg+wsJC8f33tdGmTQelfYn1r/v37yIsLBS5c+eGh0dpBAc/QvHiJfHo0QOEhYXBzc0NcrkcgiAgLCwMMpkMNWvWhlwuw8OHQRCE1OkakpOT8fTpEwgCUKzYN4iM/AA3NzeEh4chMjISrq6ucHHJhSdPHklpcHR0ZOyG0pWdYx+MnxkmOvoTRo0ahA8fPsDZ2Rl58371/23nUgAEBAXdR1hYKFJSBMTGxiAxMRE5c7oCAOLiYuHo6ITateuiTZsOAP6LvQcF3Ud4eDi+++57WFvbQC63ktrvKSkp8PQsDbk89V3zlJRkheVrARAQEHARefLkxeDBI7BixWIAwKBBw7F8+SKEhr5Hnjx5ULp0ObRu3VbaL6BcLmlqO2v7XPH5wMOHD6T4hC5lkKYyR11ZmRnqCepkhnSpi8PoumxmOY+U+WS7zhZPnz7F0qVLcfXqVURGRqJw4cLo1KkTunbtKmW+ih4/fozly5cjICAA0dHRyJ8/Pxo1aoQBAwbA1dU14w8gE/LzO46tWzcBAFJS5GjatIVe6+7duwMA4OBgr9e6WV1WPXZD0512PUGATteNLvtTXEa0cuUSjBw53mTHobj8s2fBuHLlktL34jFpSoe6NN69extOTk64d+8u7t27AwA4cuSw9P2hQ/vTTZ+mdL1/H4KVK5egVKmyKvsVqTuO9I5HMV2iuLhY6e8HD+5j5sypePnyhfRZUlISDh3yxqtXLzF69AS1+yPLyJXLAbACwhPDseLNCvh98MNnQbkh6PfBDyvfrETjXI0xqMAg5LHJg1y5HPDhQ5yFUp05nX36ELffvYarqysGDBiQ7vIDBgzA8ePHcevtK5x7+gh1v/HIgFQSZS1+fsfh7b0bQGqZeelSAF69+q98iY2NVSo3ASA+Pg7x8f/lT2FhoQCAxMREHDlySPr8/v27ePHimVQO3r17W/pOsXwMCLgg7VOxfHNwsMepU//i/fsQpf2I5bnI0dERnz7Fq5SnYtmurn6kqdxOr76S9nw5OTmpLK9rvSq97ZiDJevGWbVerq/scpxERNlBZsjTzZUGcx6bphiKoXUeTdtLu+3LlwOkeuSBA/vg7OyitC/F+hcAhIa+x4MH96X1FT9P69Ahb61pfPQoSGXdsLAwhIUpjzQQGxuLQ4e84ed3FG3adETz5q3SOXrKjrJr7IPxM8PNmDEVISGp7ebw8HCEh4cDUG07KwoP/y9/io2NlfJNde1lxTh1YOAFKa+9f/+u2m0r5pnv34dg0qQxiIyMBAClv0ND3+P+/XtwcnJS2q9iuaSp7aztc3Xt/fv37+pUBmkqc9SVlZmhnqBOZkhX2jT06NFF52Uzy3mkzCdbTSPy4MEDtG/fHr6+vihQoADq1KmDd+/eYcaMGRg3bpza5Tt06IAjR44gb968qFevHgBgw4YN+Pnnn6VCIjv79OmjUmNg377d+PTpo9nXzeqy6rEbmu606+3duwv79u1Kdzu67C/tMqIrVy7h7dvXJjmOtMur66CQ9pgUl338OEhtGgFg9+4duHpVfYeH9NKXXrquXLmEfft2atyupo4WgObj0YViRwtF165dzhLXeXaRM6cdYAU8jX+KHg96wCfCR6WhKPosfIZPhA96PuiJJ/FPILOSpa5PAID4xESsu3QOADB48GC4uLiku07OnDkxePBgAMDaS2cRn5ho1jQSZTWfPn1UKYcUO1qYgqZyUPFzxX0qlm+7d+/QKT2bN29WW57u27cbb9++1lo/UreOpnJU3fnau3eX0vK61qvS2445WLJunFXr5frKLsdJRJQdZIY83VxpMOexpRdD0bfOo217abedNk6yZ89OaV/q6l+WFBcXpzWWRNlXdo19MH5muMePg0zWjt+9e0e6eaWmmLQ2YueKtH+L9u7dqfY5hqa289u3rzV+run5gLiMoc8n0paVmaGeoE5mSJe6NERFRem8bGY4j5Q5ZZvOFoIgYNy4cYiOjsZff/2FHTt2YNmyZTh+/Dg8PT1x+PBhHD9+XGmd0aNHIzY2Fr///jsOHTqEZcuW4dixY+jUqRPevn2L+fPnW+hoMo+9e3chNjZG+n9sbIzODQRj1s3qsuqxG5rutOvFxcUqjYagaTu67C/tMooWL1Z/j+p7HNr2IUp7TIr++mu2xvU/f47Xul1t6dMtXYb1ntZ2PMbICtd5diG3liM8MRyDHw1GeFK4TuuEJYVh8KPBCEsMg9w621Qh0rXr5mWExUSjdOnSaNVK97d/WrdujVKlSiEsJhq7b102YwqJsp69e3eZpRwyFV3KbwCIiYlRexyxsTFYtGi+1vqRunU0laPqzldcXKzS8rrWq9LbjjlYsm6cVevl+soux0lElB1khjzdXGkw57GlF0PRt86jS0xGk/j4OGlfmbHe+/kzpz4gVdk19sH4meH++mu2ybb1+XO8RfLKuLg4tc8xNLWdFy2ar/FzY8ogbWVO2rIyM9QT1MkM6VKXhi1btui8bGY4j5Q5ZZuc/sKFCwgKCkL16tXRuvV/8/C4ubnhjz/+AAClm+ru3bt4/PgxSpUqhV69ekmfW1lZYfjw4QCAc+fOZVDqM6eXL1/g33+Pq3z+778n0u2xaMy6WV1WPXZD061pvfS2o8v+0tv2y5cvcOVKoFHHoWv6tYmJiTZqfUD385PZnTx5PFNf59mFq6s9rOXWWPFmhc4NRVF4UjhWvlkJa7k1XF3tzZTCrOPNx0jsvX0VADB27FhYWVnpvK6VlRXGjh0LANh7+yreflTfk5oou3n58gVOnjxm6WSYnSHlobr6irbzdfJk6vK61qvS2445WLJunFXr5frKLsdJRJQdZIY83VxpMOex6RpD0TVmYYqYjJ/fMVy5EphpYzusI5Ci7Br7YPzMcEeP+pgkJp4ZnTx5QmPbWVPeqUueqqndrUuZI5aVmaGeoE5mSJemNPj4+ODZs2c6LWvp80iZ1xfR2eLgwYPo2rUrKleujAoVKqBly5ZYvXo14uP/e+NM7BjRqFEjlfWrVKmC3Llz4+rVq4iOTi0AypYti/Pnz2Pp0qUqy8fGpvZMs7a2NsfhZAmCIGDr1o0QBEHlu5SUFI3fGbtuVpdVj93QdGtbT9t2dNmfrudr7doVSElJMeg49Em/uel6fjI7QRCwZUvWTPuXxNraGp+SPsHvg59B65/4cALRydHZuhwUrQ08i6SUZDRv3hwVKlTQe/1vv/0WP/30ExKTk7E28IwZUkiUtaSWE/9YOhmZVtr6SnrnSxBSl9+y5Z9061W6bMfU5bcl68ZZtV6ur+xynERE2UFmyNPNlQZzHps+MRSxbqVtWVPGZFavXp5py2HGbkhRdo19MH5mmOTkZGzfvtnSyTAbQUgx23YNfT4hlpXptf0tka9nhfrL6tWrleIslk4vZT1ZurNFSkoKRo8ejXHjxuHOnTuoVKkS6tSpg/fv32PBggXo2rUrPn5MnUPn8ePHAAAPDw+12ypWrBhSUlIQHBwsfZY3b14ULlxYablPnz5h5syZAKDXcFlfmuvXr+LOnZsav799+yZu3Lhm8nWzuqx67IamO731NG1Hl/15e+/WadvR0dHw9t6jU3rSHoe+6Tc3Xc9PZnfnTua8zrOTFKTgQPgBjXNMpuez8BkHwg4gBeZpXGQVV149Q8CLJ3BydMTQoUMN3s6wYcPg6OgI/xdPcPXVM9MlkCgLun79Ku7evWXpZGRqivUVXc7X7ds3tS4j1qt02Y6py29L1o2zar1cX9nlOImIsoPMkKebKw3mPDZ9Yyh37tzSuq/AwECTxWQMnYYkIzB2Q6LsHPtg/MwwK1YsQkpKsqWTkSUZ83xCl7a/JfL1rFB/uXr1qlKcxdLppawnS3ep27p1K3x8fFC0aFGsX78eBQsWBJD6cHX06NE4ffo0/vjjDyxcuBDv378HkNqBQh3x87CwMLXfHzx4EAcOHMCNGzcQFxeH9u3bY+TIkWY4qswvKSkR27dvSne5bds2onz5CrC2tjHJulldVj12Q9Ot63ppbd2q29ushw5567zNQ4f2oWnTn/Q6DgAGpd/cdD0/md38+aabs4/0c+DAAVjJrHA39q5R27kbexdWMis4Oek+dOSXJDE5GasCUt/G+LVfP+TJk8fgbeXJkwe//vorlixZgpUBZ7CyTSHY6DEk55ekW7d2lk4CUZawbdtGlC5dBtu2bTTJ9g4e3KfTclu3/mOyeqol68ZZtV6ur+xynERE2UFmyNPNlQZzHpsxsSl1+0pMTMSaNWv03l5WxdgNZefYh5OTlcnjZz///LNpEkdfNLEMAkz/fCKj235Zrf5SunQZi6eXsqYsPbLFxo0bAQBz586VOloAQI4cOTB//nw4Ozvj6NGjePPmDeLi4gAA9vbq58cSPxenCEnrzJkzuHjxImJjYyGXyxEZGYl3796Z8GiIiIjMTxy6MDZZfXmnK3H97DYUornJLJ0AIiIiIiIiIiIzygqxD8bPiIhIV1m2s8Xbt2/x+vVr5M+fH5UqVVL53tnZGXXr1oUgCLh8+TLk8tRDlcnUF+WK8/GoM2HCBNy+fRtHjhxBixYtcPLkSXTt2hVRUVEmOqKsw9raBl279kp3uW7deqv07DJm3awuqx67oenWdb20unfvg27deqe7XKtWbXXeZqtW7ZAjh7Nex2Fo+s1N1/NDpElSUhIAwNHK0ajtiOuL28tubKysMPC7egCAtWvXahwZSxehoaFYt24dAGDgd/Uy9ZsdRJQ5dOvWG/b2DiarE7RurduoMt279zFZPdWSdeOsWi/XV3Y5TiKi7CAz5OnmSoM5j82Y2JS6fdnY2KB///56b48oq8rOsQ/Gz8hSxDLIHM8nMrrtl9XqL/b2DhZPL2VNWbY7nTgtiLu7u8ZlxNEuwsLC4OiYWqjFx8erXfbz59R5t8Tl0vrqq68AAMWLF8dff/2FyMhInDlzBjt37sSAAQMMO4gsrFKlKihX7luNcxeVL/8tKlasbPJ1s7qseuyGpju99bRtJ739tW3bEY8ePUx32zly5EDbth0MOg59029uup6fzK5cuW8xYcIUjZ3fdJE3rzMAIDT0k6mSlW3ExCTDziEZZR3Lwu+Dn8HbKetYFslCMmJisu8cjFULFsV3hb9BwIsnWLp0KaZPn27QdpYuXYrY2FjULPwNqhQsatpEZjHbtuk2lUFGY56TcQRBwJw5f2qdZzS7U6wPVKpUBWXLVtB6vsqX/xYpKYLGZcR61cOHQelux9T1VEvWjbNqvVxf2eU4iYiyg8yQp5srDeY8Nn1jO+XKVdC6rxo1apgsJuPo6ITY2Bijt2MOpojdkO4y85Sa2TX2YY74WWaNeZhacnIyevfugpSU7BszNJQxzyd0aftbou2XFeo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2qEkuV4EWLpznV9oFC+bq2mvby2oNjV1MXK4CLVr0XrDDqLRVq5YHO4Sgmzz5lWCHAJTL5Ta07t+FkwS/+93vlJCQEHBZCQkJeuihhzR16lSt+3e2xg6LlNViqq5QQ9bw4YODHYLPihUrZDFZlOKo2sK0FEeKLCaLYmIsGjhwYPUEh7C3cOE8X/8o0L6JP30D73UkBdz/OT/W6hRIvWsqFgBA3ZGb69Tq1R9VmG7VqmW68867FB1tqzBtTbbl5edfpoSE0h+KCldHjx4JqTGBxLggUHVtfBxq9y0uLKE0JqvJeAAAdVPY7mzx008/6dixY7rooovUoUOHEufj4uLUrVs3GYahbdu2yWwurKrJVHpH1rt11PlbSD311FN64YUXNHbsWG3YsEEzZ87UunXr9Kc//Uk//vijfvvb3+r06dM1UDsAAC4cZTS9qGHeLX4dbkcFKcvnzV8XtwwGAAAAwh3jgtDC+BgAAODCEraLLbyvBWnWrFmZaby7XaSnp8tut0uScnNzS02bl1e4hZs33WeffaaPP/5Yt956qx544IEiae+55x7dfffdOnv2rD744IOqVQRBYbVGaPjw+/1KO2LEAyG1ytVqjVBS0qhgh1Fpd901KNghAKiA1WJS31tiJUlvv/12kVdrVdapU6f097//XZJ0x82xIffUTl3gcrkkSXaLvUrlePN7ywMkafjw+339o0D7Jv70DbzXqUr/5/xYq1MgMdVULACAuiM62qb+/e+uMN1ddw32a1cLqWbb8vLzD9aIEQ9UnBBVwrggMIyPgeoTSmOymowHAFA3he1SZO8OFGXtVCH98m7IyMhINW7cWKmpqUpPT9cVV1xRIu2pU6ckSYmJhdsXbt26VZJ00003lVr2rbfequXLlys1NTXwSiCoOnTopHbt2isl5T9lprn22uv0q191rMWo/NOhQyddc811fr+PLjY2tsz3tJd3rrpce+11GjRoqPbv31dt72KNjY3VJZdcqtTUqm2DWVuuuaa9nnnmf8r9zoL/EhPjJEmnTmXV+rUv9K01W7WIUuuWkdr7g0PTpk3TSy+9FFA506ZNk8PhUOuWkWrVIqqaowxdCxcuC3YIPjk5bkXZ3Gpnb6fkM8kBl9PO3k5uw62cHHdI1a+mGYahV199SSkp/r+LNtRVV5tfWv+osn0Tf/oGxa9T2WuUFWt1qkxModqvBACEn8GDhyk5+WPl5JQ9zh806J5KlVkTbbnFYvHNzZUVo8lkqnT7HsrmzfuHIiJC6w944TQuCLXxdl0aH9elsV518ng8evTRB8r8Pr4QxcbGqkWLy7Rnj39j5VAak9VGPACAuidsd7Zo3LixJOnIkSNlpvGei4+PV6tWrSRJBw4cKJHOMAwdPHhQFovFtxDj3LlzksreGs97vKCgIMAaINhMJpPuu6/sJyhMJrNGjLg/JP84bjKZNHKkf7GZTGb97ndjSk1rNpd9rrqYzYW/R7PZ7HfM/vjd78bo/vsfqpayalrhv9cDIXkvAaXpc1OsrBZp7dq1+vbbyk96fvvtt1q3bp2sFqnPzXE1ECH8ZZZZA+MHKsoU2IRelClKAxMGyhy+XcaAFfYTHgx2GNWmvP5AZXjb9eLlVKZv4k/foLTrVOYa5cVanfyNqTZiAQDUHWazWQ8/PKbM87/73Rjf63T9VRNteXlPG3tjrGz7Hspuu617yC208GJcEDjGxyhPRd/HF6Lf/W6MRo16sFLtRSiMyWorHgBA3RO2PeSmTZuqWbNmOnHihHbt2lXifFZWlrZs2SKz2azOnTura9eukqTNmzeXSLtz506dPn1anTp1Umxs4fZwl19+uSTp008/LfX6W7ZskSS1bt26WuqD4GjevIV69OhT6rkePXqpefMWtRyR/5o3b6Hu3XtXmK5Hj17q3PmGUtN27172uYrExMT6la57919+j/7GXJFLLmmhzp1vKPffL5T06NE7pO8loLhG9a266brCLWInT55c5tNopXG73Xr99dclSTf/yq5G9Sw1EiP843K5FGeNU8+GPQPK36thL8VaYuvMVsHFhUs744/y+gOVcX67Xpy/7bw/fYOyrlNW+tLSlhdrdfKn3rUVCwCg7vCOiYvzjpcDUd1teZ8+/fyKsbrmCoLJ+zBLqGJcEDjGx6hIWd/HF6Lz52Qr217UpFCLBwBQt4TtYgtJGjWqcIX8M888o6NHj/qO5+TkaOLEicrOzlavXr3UuHFjdenSRa1atdKWLVu0ZMkSX9rTp0/7toB74IFfdjno37+/7Ha7PvvsM82fP7/IddetW6fFixcrKipK99xTuW0ZEXqGDBkmm63oeyttNrsGDx4WpIj8N2TIMNntMWWeP78exdPa7TFlnrPZ7EV+J8U/2+0xmjTpuQrjO/8aZcUcExNT4vd//nWjoqJLHH/iiaeLlFfae2gjI2t+W8bf/vZRv9KFw70EFNetY4zqxZiVmpqqVatW+Z1v5cqV2rt3r+rHmtW1Q9nfT6gdZ8/myuVxaUzTMYq3xlcqb4I1QaObjpbL49LZs7k1FGHoK+wn+Pe+8/PVRjvkr+L9gdLaXZvNVuF73Utr14urqE/hT9+gouuUlv7JJ5+uVBnVrbw+WW3HAgCoO5588ukSx84fLweiuttyf2OsaH4j1I0Z80SwQygX44KqYXyMipT2XVfdoqNtpc6TlkwXXeZcqyRFRZV/vjzF52TL+94O9pjMn/YLAIDqENaLLUaOHKk77rhDP/zwg/r27avf/va3GjdunLp3765PPvlEbdu21YsvviipcIX5K6+8Irvdrj/+8Y8aOnSoxo0bpz59+igtLU1Dhw7V7bff7is7Pj5er7/+uiIjI/WnP/1Jffr00WOPPaYBAwZo/PjxMpvNevXVV9W8efMg1R7VJS6uXqkTBXFx9YIUkf/i4upp0KChvs+dO3cpcv78ehRPO3jw0DLPDRkyrMjvpPjnwYOH6sorr1anTr9c75JLSq4KPv8aZcV83333FSn7/DoMGTJM99xzb5H8nTt30cUXNytS3pAhvylx7WHDkorEV1n16zco93yDBg10++09i1yjQYOSeaxWa1jcS0BxkREm9b6pcAebGTNm+F6vVZ7MzEzNmDFDktT7xlhFRrAtYyjwuDxKiEjQjFYz/J5YTbD+nD4iXu4C/5/cuhDFxdXzLfCtjGHDkqrtiZnz29ji7W3xtr80xfsDpU0wDRnym1Lb0/OV1q4XV1Gfwp++QUXXKS39xRc3q1QZ1a28PlltxwIAqDsuvrhZkTFp8fFyIKq7Lfc3xuJllKW0cXdFyutLVZcbb7ylRsqtTowLAsf4GBW5+OJmlX59U2nKG9/dc89vSsyTlp7u3iLf28W/94YOvbfCRQelxVHanGx539vBHpP5034BAFAdrMEOoCrMZrPeeOMNdevWTUuWLNHOnTslSS1bttTDDz+sESNGKDIy0pe+ffv2+vDDDzV16lR9/fXX2r9/vy699FI99dRTpe5Q0aNHDy1dulRvv/22vvrqK33yySeKi4tT79699fDDD+uaa66ptbqiZvXs2VsOR47S0lJ19dVtwmrL8J49eys3t/DJgjvu6Ke1a1eVWY/z01Z8zqjgszRmzOOaMuXPkqTHH39ayckfy+NxSzLJbDaX+Xv0XismJlL9+/fXqVPnitRh/fq1Ra6bnX1OX3zxbyUkJGr06JJPi/Ts2VvZ2ee0ZcvnkqSbb+6mHj366Lbbuuv11/9PBw7sV0REpGJiYpSQkCDDkA4f/lEmk9SixaW66qo2+v77/briilY6cGCfTCaTxo59QtOnv6lTp06qQYOGOnTooCRDkhQZGalXXvlrid/B+XlMJpMyM8/q7rsrniwCQtU1V0RpW0qEfjh+VnPmzNHEiRPLTT9nzhxlZmaqZdMItbsidJ7qr+syM/PUsKFNl0dfrvmt52vW8VnaeGaj8oy8EmmjTFHq1bCXRjcdrfiIeMktnTuXH4SoQ0v//v315Zdf6ttvv5XZbPn5/eKSyWSW1WpRixYt1bp1W0nS/v1puvrqturRo49uueVWPf/8JGVnZysmJkaJiY3VqtVVOnBgvyTpqqta+ybkPB639u/fp1atrpYk7d2booyMDCUmNtYTTzyt5OQNkgrbvLfemqz09FO66aau6t9/oFav/ujnNtBQfHyC7/2zJpNZrVu3LbXNdzhylJqaIpPJdF4aw3dcMmQymX+O0VJuu15cxX0Kf/JU9hqVL6O6Fe+TffZZsiTp9tvDp18JAAg/549JSxsvB6K623J/Y+zZs7csFo/cbrccjsI+qMvl0hdffC6Hw6FLL22pxx4br2nTpujHH3+U3W7XTTd1ldUaofz8XP3zn5uUn5+nyy+/UhaL5ed+Tjv17t1XU6dOliQ9/vgEJSdvkMuVr7S0VKWnp6tRo0bKyMjQmTOnFRkZpRYtWshsLsx/5ZVXad++VB0+fFg2m005Odlyu92KiYlVZuZZGYahgQPDY9dbxgVVw/gYFRk0aJg++uhDRUREqKAgXx6PR40axevGG2/RwYMHZBiF84oej1uHD/8op9MpqfBvHI0axeuWW25T//4DtXbtSqWm7tapU6dkMpmUkJCoNm2u8X0fnzuXqU2bNkgydPvtvWQ2m/Xll/+WxWJW9+7dS3xv9+zZR2+99bpvDHn+ee88rmTI4/H4xrP9+t2l9evXyOXK1759aTKZTGXOyf5Sjlf588I1KZD2CwCAqjIZ3lYeISU/36XMTGeww/BbYmKcJOnUqawgR4Jwwn2DQAXz3hk+fLDv5yeHhfWaRb/8N8OlWR+eltls1sKFi3TllVeWmm7//v0aPny4ZHg0+p5GahJ/4f9uvN5c/Mt7ixcuXBbESMpXv36UzFazrGarst3ZWpG+QimOFDncDtktdrWzt9PAhIGF72L2uOQucNf5CVUv2isEgvsGgeLeCR3ef4vaUJtzANxjCGXcnzUvlMcFoT7evtDGx+EyloV/+P5EqOLeRCjj/kRxgcwDhGZPDwA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+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 355,
+       "width": 1069
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_xkhat_vs_xk(\n",
+    "    df_xk: pd.DataFrame,\n",
+    "    xkhat: str = \"xk_unobs_ppc_data_domain\",\n",
+    "    x: str = \"xk_unobs_true_val\",\n",
+    "    mdlnm: str = \"mdla\",\n",
+    "    in_samp: bool = True,\n",
+    ") -> plt.Figure:\n",
+    "    \"\"\"Convenience plot forecast xkhat with overplotted x true val (from synthetic set)\"\"\"\n",
+    "    g = sns.catplot(\n",
+    "        x=xkhat,\n",
+    "        y=\"oid\",\n",
+    "        col=\"xk_nm\",\n",
+    "        data=df_xk.reset_index(),\n",
+    "        **KWS_BOX,\n",
+    "        height=5,\n",
+    "        aspect=1.5,\n",
+    "        sharex=False,\n",
+    "    )\n",
+    "    _ = g.map(sns.scatterplot, x, \"oid\", **KWS_SCTR, zorder=100)\n",
+    "    s = \"In-sample\" if in_samp else \"Out-of-sample\"\n",
+    "    _ = g.fig.suptitle(\n",
+    "        f\"{s}: boxplots of posterior `{xkhat}` with overplotted actual `{x}` values\"\n",
+    "        + f\" per observation `oid` (green dots) - `{mdlnm}`\"\n",
+    "    )\n",
+    "    _ = g.tight_layout()\n",
+    "    return g.fig\n",
+    "\n",
+    "\n",
+    "_ = plot_xkhat_vs_xk(df_xk_unobs, mdlnm=\"mdla\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Here's our auto-imputed posterior distributions (boxplots) for the missing data on the in-sample dataset `dfx_train`\n",
+    "+ These are a (very helpful!) side-effect of our model construction and let us fill-in the real-world missing values for\n",
+    "  `c`, and `d` in `df_train`\n",
+    "+ Some observations (e.g. `o03`) have missing values in both `c` and `d`, others (e.g `o04`) have only one\n",
+    "+ We also overplot the known true values from the synthetic dataset: and the match is close for all: usually \n",
+    "  well-within the HDI94\n",
+    "+ Where observations have more than one missing value (e.g. `o00`, `o8`, `o10` are good examples), we see the\n",
+    "  possibility of a lack of identifiability: this is an interesting and not easily avoided side-effect of the data and\n",
+    "  model architecture, and in the real-world we might seek to mitigate through removing observations or features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.5 Create PPC Forecast on `dfx_holdout` set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**IMPLEMENTATION NOTE**\n",
+    "\n",
+    "The following process is a bit of a hack:\n",
+    "\n",
+    "1. Firstly: We need to re-specify the model entirely using `dfx_holdout`, because (as noted above $\\S 2.1$ \n",
+    "  **Build Model Object** ) we can't put `NaNs` (or a `masked_array`) into a `pm.Data`. If we could do that then we \n",
+    "  could simply use a `pm.set_data()`, but we can't so we don't.\n",
+    "2. Secondly: Sample_ppc the `xk_unobserved`, because this is a precursor to computing `yhat`, and we can't specify\n",
+    "   a conditional order in `sample_posterior_predictive`\n",
+    "3. Thirdly: Use those predictions to sample_ppc the `yhat`\n",
+    "\n",
+    "**REALITIES**\n",
+    "\n",
+    "+ This process is suboptimal for a real-world scenario wherein we want to forecast new incoming data, because we have to\n",
+    "  keep re-specifying the model in Step 1 (which opens opportunities for simple human error), and Steps 2 & 3 involve \n",
+    "  manipulations of idata objects, which is a faff\n",
+    "+ It should still be suitable for a relatively slow, (potentially batched) forecasting process on the order of seconds, \n",
+    "  not sub-second response times\n",
+    "+ In any case, if this were to be deployed to handle a stream of sub-second inputs, a much simpler way to rectify the \n",
+    "  situation would be to ensure proper data validation / hygiene upstream and require no missing data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5.1 Firstly: Rebuild model entirely, using `dfx_holdout`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jon/miniforge/envs/oreum_survival/lib/python3.11/site-packages/pymc/model/core.py:1366: ImputationWarning: Data in xk contains missing values and will be automatically imputed from the sampling distribution.\n",
+      "  warnings.warn(impute_message, ImputationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "COORDS_H = deepcopy(COORDS)\n",
+    "COORDS_H[\"oid\"] = dfx_holdout.index.values\n",
+    "\n",
+    "with pm.Model(coords=COORDS_H) as mdla_h:\n",
+    "    # 0. create (Mutable)Data containers for obs (Y, X)\n",
+    "    # NOTE: You could use mdla.set_data to change these pm.Data containers...\n",
+    "    y = pm.Data(\"y\", dfx_holdout[ft_y].values, dims=\"oid\")\n",
+    "    xj = pm.Data(\"xj\", dfx_holdout[FTS_XJ].values, dims=(\"oid\", \"xj_nm\"))\n",
+    "\n",
+    "    # same code as above for mdla\n",
+    "    # 1. create auto-imputing likelihood for missing data values\n",
+    "    # NOTE: there's no way to put a nan-containing array (nor a np.masked_array)\n",
+    "    # into a pm.Data, so dfx_holdout[FTS_XK].values has to go in directly\n",
+    "    xk_mu = pm.Normal(\"xk_mu\", mu=0.0, sigma=1, dims=\"xk_nm\")\n",
+    "    xk = pm.Normal(\n",
+    "        \"xk\", mu=xk_mu, sigma=1.0, observed=dfx_holdout[FTS_XK].values, dims=(\"oid\", \"xk_nm\")\n",
+    "    )\n",
+    "\n",
+    "    # 2. define priors for contiguous and auto-imputed data\n",
+    "    b_s = pm.Gamma(\"beta_sigma\", alpha=10, beta=10)  # E ~ 1\n",
+    "    bj = pm.Normal(\"beta_j\", mu=0, sigma=b_s, dims=\"xj_nm\")\n",
+    "    bk = pm.Normal(\"beta_k\", mu=0, sigma=b_s, dims=\"xk_nm\")\n",
+    "\n",
+    "    # 4. define evidence\n",
+    "    epsilon = pm.InverseGamma(\"epsilon\", alpha=11, beta=10)\n",
+    "    lm = pt.dot(xj, bj.T) + pt.dot(xk, bk.T)\n",
+    "    _ = pm.Normal(\"yhat\", mu=lm, sigma=epsilon, observed=y, dims=\"oid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 12.0.0 (0)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"512pt\" height=\"710pt\"\n",
+       " viewBox=\"0.00 0.00 512.33 710.18\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 706.18)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-706.18 508.33,-706.18 508.33,4 -4,4\"/>\n",
+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>clusteroid (10)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M291,-8C291,-8 365,-8 365,-8 371,-8 377,-14 377,-20 377,-20 377,-211.32 377,-211.32 377,-217.32 371,-223.32 365,-223.32 365,-223.32 291,-223.32 291,-223.32 285,-223.32 279,-217.32 279,-211.32 279,-211.32 279,-20 279,-20 279,-14 285,-8 291,-8\"/>\n",
+       "<text text-anchor=\"middle\" x=\"347.25\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">oid (10)</text>\n",
+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusteroid (10) x xj_nm (3)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M54,-231.32C54,-231.32 158,-231.32 158,-231.32 164,-231.32 170,-237.32 170,-243.32 170,-243.32 170,-317.32 170,-317.32 170,-323.32 164,-329.32 158,-329.32 158,-329.32 54,-329.32 54,-329.32 48,-329.32 42,-323.32 42,-317.32 42,-317.32 42,-243.32 42,-243.32 42,-237.32 48,-231.32 54,-231.32\"/>\n",
+       "<text text-anchor=\"middle\" x=\"105.75\" y=\"-238.52\" font-family=\"Times,serif\" font-size=\"14.00\">oid (10) x xj_nm (3)</text>\n",
+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clusterxk_nm (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157,-471.04C157,-471.04 331,-471.04 331,-471.04 337,-471.04 343,-477.04 343,-483.04 343,-483.04 343,-580.86 343,-580.86 343,-586.86 337,-592.86 331,-592.86 331,-592.86 157,-592.86 157,-592.86 151,-592.86 145,-586.86 145,-580.86 145,-580.86 145,-483.04 145,-483.04 145,-477.04 151,-471.04 157,-471.04\"/>\n",
+       "<text text-anchor=\"middle\" x=\"306.12\" y=\"-478.24\" font-family=\"Times,serif\" font-size=\"14.00\">xk_nm (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust4\" class=\"cluster\">\n",
+       "<title>cluster12</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-341.23C20,-341.23 132,-341.23 132,-341.23 138,-341.23 144,-347.23 144,-353.23 144,-353.23 144,-451.04 144,-451.04 144,-457.04 138,-463.04 132,-463.04 132,-463.04 20,-463.04 20,-463.04 14,-463.04 8,-457.04 8,-451.04 8,-451.04 8,-353.23 8,-353.23 8,-347.23 14,-341.23 20,-341.23\"/>\n",
+       "<text text-anchor=\"middle\" x=\"129.25\" y=\"-348.43\" font-family=\"Times,serif\" font-size=\"14.00\">12</text>\n",
+       "</g>\n",
+       "<g id=\"clust5\" class=\"cluster\">\n",
+       "<title>cluster8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M164,-341.23C164,-341.23 296,-341.23 296,-341.23 302,-341.23 308,-347.23 308,-353.23 308,-353.23 308,-451.04 308,-451.04 308,-457.04 302,-463.04 296,-463.04 296,-463.04 164,-463.04 164,-463.04 158,-463.04 152,-457.04 152,-451.04 152,-451.04 152,-353.23 152,-353.23 152,-347.23 158,-341.23 164,-341.23\"/>\n",
+       "<text text-anchor=\"middle\" x=\"296.62\" y=\"-348.43\" font-family=\"Times,serif\" font-size=\"14.00\">8</text>\n",
+       "</g>\n",
+       "<g id=\"clust6\" class=\"cluster\">\n",
+       "<title>clusteroid (10) x xk_nm (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M190,-231.32C190,-231.32 297,-231.32 297,-231.32 303,-231.32 309,-237.32 309,-243.32 309,-243.32 309,-317.32 309,-317.32 309,-323.32 303,-329.32 297,-329.32 297,-329.32 190,-329.32 190,-329.32 184,-329.32 178,-323.32 178,-317.32 178,-317.32 178,-243.32 178,-243.32 178,-237.32 184,-231.32 190,-231.32\"/>\n",
+       "<text text-anchor=\"middle\" x=\"243.25\" y=\"-238.52\" font-family=\"Times,serif\" font-size=\"14.00\">oid (10) x xk_nm (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust7\" class=\"cluster\">\n",
+       "<title>clusterxj_nm (3)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M363,-471.04C363,-471.04 437,-471.04 437,-471.04 443,-471.04 449,-477.04 449,-483.04 449,-483.04 449,-580.86 449,-580.86 449,-586.86 443,-592.86 437,-592.86 437,-592.86 363,-592.86 363,-592.86 357,-592.86 351,-586.86 351,-580.86 351,-580.86 351,-483.04 351,-483.04 351,-477.04 357,-471.04 363,-471.04\"/>\n",
+       "<text text-anchor=\"middle\" x=\"413.62\" y=\"-478.24\" font-family=\"Times,serif\" font-size=\"14.00\">xj_nm (3)</text>\n",
+       "</g>\n",
+       "<!-- y -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>y</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M343,-98C343,-98 313,-98 313,-98 307,-98 301,-92 301,-86 301,-86 301,-52.5 301,-52.5 301,-46.5 307,-40.5 313,-40.5 313,-40.5 343,-40.5 343,-40.5 349,-40.5 355,-46.5 355,-52.5 355,-52.5 355,-86 355,-86 355,-92 349,-98 343,-98\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"328\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- yhat -->\n",
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+       "<title>yhat</title>\n",
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+       "<text text-anchor=\"middle\" x=\"328\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- yhat&#45;&gt;y -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>yhat&#45;&gt;y</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"331.5,-109.79 328,-99.79 324.5,-109.79 331.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- xj -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>xj</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M150,-321.32C150,-321.32 120,-321.32 120,-321.32 114,-321.32 108,-315.32 108,-309.32 108,-309.32 108,-275.82 108,-275.82 108,-269.82 114,-263.82 120,-263.82 120,-263.82 150,-263.82 150,-263.82 156,-263.82 162,-269.82 162,-275.82 162,-275.82 162,-309.32 162,-309.32 162,-315.32 156,-321.32 150,-321.32\"/>\n",
+       "<text text-anchor=\"middle\" x=\"135\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">xj</text>\n",
+       "<text text-anchor=\"middle\" x=\"135\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"135\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- xj&#45;&gt;yhat -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>xj&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M148.12,-263.45C154.56,-252.17 163.27,-239.85 174,-231.32 203.76,-207.66 244.46,-193.53 276.54,-185.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"277.11,-188.89 286.02,-183.16 275.48,-182.09 277.11,-188.89\"/>\n",
+       "</g>\n",
+       "<!-- xk_mu -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>xk_mu</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"194\" cy=\"-544.2\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-555.65\" font-family=\"Times,serif\" font-size=\"14.00\">xk_mu</text>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-539.15\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"194\" y=\"-522.65\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- xk_observed -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>xk_observed</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"76\" cy=\"-414.38\" rx=\"60.1\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"76\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">xk_observed</text>\n",
+       "<text text-anchor=\"middle\" x=\"76\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"76\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- xk_mu&#45;&gt;xk_observed -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>xk_mu&#45;&gt;xk_observed</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M166.63,-513.56C151.28,-496.93 131.87,-475.91 114.96,-457.59\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"117.77,-455.47 108.42,-450.5 112.63,-460.22 117.77,-455.47\"/>\n",
+       "</g>\n",
+       "<!-- xk_unobserved -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>xk_unobserved</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"230\" cy=\"-414.38\" rx=\"69.65\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"230\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">xk_unobserved</text>\n",
+       "<text text-anchor=\"middle\" x=\"230\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"230\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- xk_mu&#45;&gt;xk_unobserved -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>xk_mu&#45;&gt;xk_unobserved</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"219.14,-466.86 218.48,-456.29 212.4,-464.97 219.14,-466.86\"/>\n",
+       "</g>\n",
+       "<!-- beta_k -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>beta_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"294\" cy=\"-544.2\" rx=\"41.01\" ry=\"40.66\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"294\" y=\"-539.15\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"294\" y=\"-522.65\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_k&#45;&gt;yhat -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>beta_k&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M304.09,-504.3C307.11,-491.27 310.11,-476.61 312,-463.04 323.44,-380.85 326.77,-284.37 327.7,-226.72\"/>\n",
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+       "</g>\n",
+       "<!-- xk -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>xk</title>\n",
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+       "<text text-anchor=\"middle\" x=\"231\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"231\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- xk_observed&#45;&gt;xk -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>xk_observed&#45;&gt;xk</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M104.3,-378.44C116.54,-365.21 131.78,-351 148,-341.23 158.35,-334.98 163.13,-338.52 174,-333.23 177.46,-331.54 180.95,-329.65 184.41,-327.65\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"186.03,-330.76 192.7,-322.53 182.35,-324.8 186.03,-330.76\"/>\n",
+       "</g>\n",
+       "<!-- xk_unobserved&#45;&gt;xk -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>xk_unobserved&#45;&gt;xk</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M230.33,-373.49C230.44,-360.46 230.56,-346 230.67,-333\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"234.17,-333.1 230.75,-323.08 227.17,-333.05 234.17,-333.1\"/>\n",
+       "</g>\n",
+       "<!-- xk&#45;&gt;yhat -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>xk&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M254.48,-263.51C266.59,-249.04 281.57,-231.14 294.84,-215.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"297.3,-217.8 301.03,-207.88 291.93,-213.3 297.3,-217.8\"/>\n",
+       "</g>\n",
+       "<!-- beta_sigma -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>beta_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"347\" cy=\"-661.52\" rx=\"55.33\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-672.97\" font-family=\"Times,serif\" font-size=\"14.00\">beta_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-656.47\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"347\" y=\"-639.97\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- beta_sigma&#45;&gt;beta_k -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>beta_sigma&#45;&gt;beta_k</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"318.75,-590.67 311.39,-583.05 312.39,-593.6 318.75,-590.67\"/>\n",
+       "</g>\n",
+       "<!-- beta_j -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>beta_j</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"400\" cy=\"-544.2\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-555.65\" font-family=\"Times,serif\" font-size=\"14.00\">beta_j</text>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-539.15\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"400\" y=\"-522.65\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_sigma&#45;&gt;beta_j -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>beta_sigma&#45;&gt;beta_j</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M364.5,-622.44C368.98,-612.7 373.84,-602.12 378.49,-592\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"381.61,-593.6 382.61,-583.05 375.25,-590.67 381.61,-593.6\"/>\n",
+       "</g>\n",
+       "<!-- epsilon -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>epsilon</title>\n",
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+       "<text text-anchor=\"middle\" x=\"449\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">epsilon</text>\n",
+       "<text text-anchor=\"middle\" x=\"449\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"449\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">InvGamma</text>\n",
+       "</g>\n",
+       "<!-- epsilon&#45;&gt;yhat -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>epsilon&#45;&gt;yhat</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"367.89,-209.03 358.25,-204.64 363.05,-214.08 367.89,-209.03\"/>\n",
+       "</g>\n",
+       "<!-- beta_j&#45;&gt;yhat -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>beta_j&#45;&gt;yhat</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M392.32,-503.81C380.78,-444.64 358.27,-329.3 339,-231.32 338.66,-229.57 338.31,-227.8 337.95,-226.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"341.42,-225.5 336.05,-216.37 334.56,-226.86 341.42,-225.5\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x142b52310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "point={'xk_mu': array([0., 0.]), 'xk_unobserved': array([0., 0., 0., 0., 0., 0., 0., 0.]), 'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0.]), 'beta_k': array([0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n",
+      "point={'xk_mu': array([0., 0.]), 'xk_unobserved': array([0., 0., 0., 0., 0., 0., 0., 0.]), 'beta_sigma_log__': array(0.), 'beta_j': array([0., 0., 0.]), 'beta_k': array([0., 0.]), 'epsilon_log__': array(0.)}\n",
+      "\n",
+      "No problems found\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(pm.model_to_graphviz(mdla_h, formatting=\"plain\"))\n",
+    "assert_no_rvs(mdla_h.logp())\n",
+    "mdla_h.debug(fn=\"logp\", verbose=True)\n",
+    "mdla_h.debug(fn=\"random\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5.2 Secondly: sample PPC for missing values `xk_unobserved` in out-of-sample dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE** \n",
+    "\n",
+    "+ Avoid changing `ida`, instead take a deepcopy `ida_h` , remove uneccessary groups, and we'll use that\n",
+    "+ We won't create a bare `az.InferenceData` then add groups, because we have to add all sorts of additional subtle \n",
+    "  info to the object. Easier to copy and remove groups\n",
+    "+ The `xarray` indexing in `posterior` will be wrong (set according to `dfx_train`, rather than `dfx_holdout`),\n",
+    "  specifically dimension `oid` and coordinate `oid`.\n",
+    "+ Changing things like this inside an `xarray.Dataset` is a total nightmare so we won't attempt to change them. It won't\n",
+    "  matter in this case anyway because we won't refer to the `posterior`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1MB\n",
+       "Dimensions:              (chain: 4, draw: 500, xk_nm: 2,\n",
+       "                          xk_unobserved_dim_0: 24, xj_nm: 3, oid: 30)\n",
+       "Coordinates:\n",
+       "  * chain                (chain) int64 32B 0 1 2 3\n",
+       "  * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
+       "  * xk_nm                (xk_nm) &lt;U1 8B &#x27;c&#x27; &#x27;d&#x27;\n",
+       "  * xk_unobserved_dim_0  (xk_unobserved_dim_0) int64 192B 0 1 2 3 ... 21 22 23\n",
+       "  * xj_nm                (xj_nm) &lt;U9 108B &#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;\n",
+       "  * oid                  (oid) &lt;U3 360B &#x27;o01&#x27; &#x27;o02&#x27; &#x27;o03&#x27; ... &#x27;o36&#x27; &#x27;o37&#x27; &#x27;o38&#x27;\n",
+       "Data variables:\n",
+       "    xk_mu                (chain, draw, xk_nm) float64 32kB 0.2089 ... 0.2972\n",
+       "    xk_unobserved        (chain, draw, xk_unobserved_dim_0) float64 384kB 0.3...\n",
+       "    beta_j               (chain, draw, xj_nm) float64 48kB 280.5 ... -55.48\n",
+       "    beta_k               (chain, draw, xk_nm) float64 32kB 23.61 46.93 ... 47.18\n",
+       "    beta_sigma           (chain, draw) float64 16kB 19.33 19.92 ... 21.89 20.18\n",
+       "    epsilon              (chain, draw) float64 16kB 0.9637 1.067 ... 1.014\n",
+       "    xk                   (chain, draw, oid, xk_nm) float64 960kB -0.4094 ... ...\n",
+       "Attributes:\n",
+       "    created_at:                 2024-12-16T10:46:11.401365+00:00\n",
+       "    arviz_version:              0.20.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2\n",
+       "    sampling_time:              181.2683072090149\n",
+       "    tuning_steps:               3000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-21b57998-497c-4674-937c-c82fa66eb389' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-21b57998-497c-4674-937c-c82fa66eb389' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>xk_nm</span>: 2</li><li><span class='xr-has-index'>xk_unobserved_dim_0</span>: 24</li><li><span class='xr-has-index'>xj_nm</span>: 3</li><li><span class='xr-has-index'>oid</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be216c11-aaf9-4751-9e29-b97dbd3335e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be216c11-aaf9-4751-9e29-b97dbd3335e8' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-af30ee14-07eb-4b8f-bf41-6667d45608ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af30ee14-07eb-4b8f-bf41-6667d45608ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c6315d3-cd52-4863-a85c-1d1d379f3cdf' class='xr-var-data-in' type='checkbox'><label for='data-4c6315d3-cd52-4863-a85c-1d1d379f3cdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-4be5f33c-6bae-4f96-9c22-81224e3be1b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4be5f33c-6bae-4f96-9c22-81224e3be1b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6c3fa00-7cd2-4b02-a7bd-0dd641c7a857' class='xr-var-data-in' type='checkbox'><label for='data-c6c3fa00-7cd2-4b02-a7bd-0dd641c7a857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_nm</span></div><div class='xr-var-dims'>(xk_nm)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;c&#x27; &#x27;d&#x27;</div><input id='attrs-550b7688-f99a-45ff-bb2b-81a02afc8dee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-550b7688-f99a-45ff-bb2b-81a02afc8dee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bde9da9f-514b-4bbb-b585-cb8b64a21a9c' class='xr-var-data-in' type='checkbox'><label for='data-bde9da9f-514b-4bbb-b585-cb8b64a21a9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_unobserved_dim_0</span></div><div class='xr-var-dims'>(xk_unobserved_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-4a8b54ed-f61d-4a2f-821c-c11cb6c30fcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a8b54ed-f61d-4a2f-821c-c11cb6c30fcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3694cd1c-7c2a-43ce-ac31-a78189fa2e06' class='xr-var-data-in' type='checkbox'><label for='data-3694cd1c-7c2a-43ce-ac31-a78189fa2e06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xj_nm</span></div><div class='xr-var-dims'>(xj_nm)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;</div><input id='attrs-4a29e56e-cfca-4930-b47f-3bf5cdbfec4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a29e56e-cfca-4930-b47f-3bf5cdbfec4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf98d34-7d57-48f7-a552-b2ce4afc5088' class='xr-var-data-in' type='checkbox'><label for='data-dbf98d34-7d57-48f7-a552-b2ce4afc5088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oid</span></div><div class='xr-var-dims'>(oid)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;o01&#x27; &#x27;o02&#x27; &#x27;o03&#x27; ... &#x27;o37&#x27; &#x27;o38&#x27;</div><input id='attrs-281ba600-f7a9-43a1-bad8-19dd07f3ab3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-281ba600-f7a9-43a1-bad8-19dd07f3ab3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bfc0df3-624c-42c0-89e0-a7c1069e56d6' class='xr-var-data-in' type='checkbox'><label for='data-0bfc0df3-624c-42c0-89e0-a7c1069e56d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;o01&#x27;, &#x27;o02&#x27;, &#x27;o03&#x27;, &#x27;o04&#x27;, &#x27;o06&#x27;, &#x27;o07&#x27;, &#x27;o08&#x27;, &#x27;o09&#x27;, &#x27;o10&#x27;, &#x27;o12&#x27;,\n",
+       "       &#x27;o13&#x27;, &#x27;o14&#x27;, &#x27;o15&#x27;, &#x27;o16&#x27;, &#x27;o17&#x27;, &#x27;o18&#x27;, &#x27;o21&#x27;, &#x27;o22&#x27;, &#x27;o23&#x27;, &#x27;o24&#x27;,\n",
+       "       &#x27;o27&#x27;, &#x27;o28&#x27;, &#x27;o30&#x27;, &#x27;o31&#x27;, &#x27;o32&#x27;, &#x27;o33&#x27;, &#x27;o34&#x27;, &#x27;o36&#x27;, &#x27;o37&#x27;, &#x27;o38&#x27;],\n",
+       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23d1aaf9-e8a6-4190-ab7a-31d29879a5af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23d1aaf9-e8a6-4190-ab7a-31d29879a5af' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xk_mu</span></div><div class='xr-var-dims'>(chain, draw, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2089 0.1731 ... -0.2048 0.2972</div><input id='attrs-c175ac04-a86d-4908-929c-4ed6a100b25d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c175ac04-a86d-4908-929c-4ed6a100b25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72dbf692-891c-4d74-b011-800935dd8544' class='xr-var-data-in' type='checkbox'><label for='data-72dbf692-891c-4d74-b011-800935dd8544' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.20891809,  0.17313158],\n",
+       "        [ 0.10968446, -0.07713499],\n",
+       "        [ 0.34814158,  0.14390001],\n",
+       "        ...,\n",
+       "        [ 0.1040242 ,  0.0791114 ],\n",
+       "        [ 0.46729255,  0.01320396],\n",
+       "        [-0.18576857,  0.00399746]],\n",
+       "\n",
+       "       [[ 0.3353833 , -0.06487559],\n",
+       "        [-0.01418716,  0.19377117],\n",
+       "        [ 0.27807151, -0.02909548],\n",
+       "        ...,\n",
+       "        [ 0.0043824 ,  0.27432789],\n",
+       "        [ 0.08791309, -0.04719615],\n",
+       "        [-0.13119566,  0.29685589]],\n",
+       "\n",
+       "       [[ 0.25890938,  0.13817301],\n",
+       "        [-0.21112456,  0.50364429],\n",
+       "        [ 0.17563658, -0.23627762],\n",
+       "        ...,\n",
+       "        [-0.09267719,  0.13752434],\n",
+       "        [ 0.27621564,  0.07859173],\n",
+       "        [-0.21930505,  0.08161181]],\n",
+       "\n",
+       "       [[ 0.30239542,  0.09277564],\n",
+       "        [ 0.57037304,  0.17265029],\n",
+       "        [ 0.73818484,  0.0257427 ],\n",
+       "        ...,\n",
+       "        [-0.0413971 ,  0.12331163],\n",
+       "        [ 0.1627879 , -0.01322874],\n",
+       "        [-0.20475786,  0.29720492]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk_unobserved</span></div><div class='xr-var-dims'>(chain, draw, xk_unobserved_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3316 1.4 -1.065 ... 0.698 0.3415</div><input id='attrs-6a1e8de8-4f0c-4198-ab2c-1cd06a97aa40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a1e8de8-4f0c-4198-ab2c-1cd06a97aa40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b74b35d-61b3-4aea-b070-b7d5c90bfa74' class='xr-var-data-in' type='checkbox'><label for='data-6b74b35d-61b3-4aea-b070-b7d5c90bfa74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.33159371,  1.40040708, -1.0653712 , ..., -0.53268713,\n",
+       "          0.63074055,  0.28655121],\n",
+       "        [ 0.26374895,  1.41843967, -0.96178183, ..., -1.33689741,\n",
+       "          1.01024666,  0.3454739 ],\n",
+       "        [ 0.67356704,  1.23985297, -1.1073857 , ...,  0.09362308,\n",
+       "          0.30041517,  0.31594624],\n",
+       "        ...,\n",
+       "        [ 1.50794207,  0.80548865, -1.03550396, ...,  0.56284223,\n",
+       "          0.03194101,  0.34082027],\n",
+       "        [ 0.99117336,  1.07671813, -1.058888  , ...,  0.81190703,\n",
+       "         -0.03141332,  0.31834347],\n",
+       "        [-0.43352998,  1.80813609, -1.0410773 , ...,  0.22429163,\n",
+       "          0.27525897,  0.27741251]],\n",
+       "\n",
+       "       [[ 1.67037953,  0.72094343, -0.95802624, ..., -0.86644935,\n",
+       "          0.71773119,  0.31022637],\n",
+       "        [-0.10049297,  1.5951994 , -1.08270023, ..., -0.36839778,\n",
+       "          0.50771374,  0.3173316 ],\n",
+       "        [-0.87007526,  1.99752237, -1.09399038, ...,  0.51241705,\n",
+       "          0.09381582,  0.30601479],\n",
+       "...\n",
+       "        [-0.93134138,  2.04282757, -1.11705007, ...,  1.49719712,\n",
+       "         -0.33292181,  0.33624381],\n",
+       "        [ 1.4983631 ,  0.82001405, -1.10844119, ..., -1.34678171,\n",
+       "          1.04991977,  0.29547717],\n",
+       "        [ 0.10682445,  1.52713878, -0.93040434, ..., -0.58400882,\n",
+       "          0.61899806,  0.32282628]],\n",
+       "\n",
+       "       [[ 1.3814937 ,  0.82372824, -0.97929634, ...,  1.7397712 ,\n",
+       "         -0.55371778,  0.31179638],\n",
+       "        [ 0.1607087 ,  1.45434902, -1.063863  , ...,  1.5941148 ,\n",
+       "         -0.45603364,  0.28632649],\n",
+       "        [ 0.44440169,  1.35847376, -1.03721039, ...,  0.91956431,\n",
+       "         -0.15502685,  0.30801239],\n",
+       "        ...,\n",
+       "        [ 0.37593545,  1.32873593, -1.00345814, ...,  1.0778376 ,\n",
+       "         -0.20802916,  0.29837661],\n",
+       "        [-0.25651817,  1.63587584, -1.06096069, ...,  0.98297266,\n",
+       "         -0.16731735,  0.29287095],\n",
+       "        [-0.23106533,  1.66683913, -1.13464076, ..., -0.72624689,\n",
+       "          0.69801981,  0.34150705]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_j</span></div><div class='xr-var-dims'>(chain, draw, xj_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>280.5 7.957 -56.4 ... 8.096 -55.48</div><input id='attrs-1d2878cd-0831-48b2-8218-1c045089be9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d2878cd-0831-48b2-8218-1c045089be9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce14fbb6-76a4-40af-a5ab-31864483a25e' class='xr-var-data-in' type='checkbox'><label for='data-ce14fbb6-76a4-40af-a5ab-31864483a25e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[280.50156619,   7.95655647, -56.40466968],\n",
+       "        [281.30548198,   8.52646745, -56.04352876],\n",
+       "        [280.57953457,   8.34469974, -55.74007472],\n",
+       "        ...,\n",
+       "        [280.68720187,   8.99938731, -55.71880658],\n",
+       "        [280.44660017,   8.67757231, -56.18057113],\n",
+       "        [281.23834165,   7.85951359, -56.45009634]],\n",
+       "\n",
+       "       [[280.74688301,   8.8760765 , -55.73783306],\n",
+       "        [281.35766155,   8.54959605, -55.72214807],\n",
+       "        [281.09571944,   8.0983401 , -56.21042462],\n",
+       "        ...,\n",
+       "        [280.78619266,   8.05854668, -55.29527511],\n",
+       "        [281.1566593 ,   8.6553639 , -56.27352171],\n",
+       "        [280.5536389 ,   9.36058137, -54.85382783]],\n",
+       "\n",
+       "       [[281.74166633,   8.04564302, -56.43724676],\n",
+       "        [281.59886996,   8.52009945, -55.98341478],\n",
+       "        [281.01564187,   8.26401633, -55.62654631],\n",
+       "        ...,\n",
+       "        [281.20781066,   8.48541819, -55.72847547],\n",
+       "        [281.49408148,   8.26841815, -55.92252308],\n",
+       "        [280.37885761,   8.70555729, -55.86262397]],\n",
+       "\n",
+       "       [[281.03786928,   8.6668038 , -56.68272186],\n",
+       "        [281.11264743,   8.4689861 , -55.61194155],\n",
+       "        [281.0122357 ,   8.66553863, -56.10891835],\n",
+       "        ...,\n",
+       "        [281.4803094 ,   8.81745181, -55.40687754],\n",
+       "        [281.41787839,   8.97202468, -55.74203196],\n",
+       "        [280.66765598,   8.09628328, -55.48191212]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_k</span></div><div class='xr-var-dims'>(chain, draw, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.61 46.93 23.91 ... 23.29 47.18</div><input id='attrs-71ad7ccc-e4e8-4990-a65a-e8db7ea77dff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71ad7ccc-e4e8-4990-a65a-e8db7ea77dff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b2f6ee-81cf-4369-a11b-99f38748bf43' class='xr-var-data-in' type='checkbox'><label for='data-70b2f6ee-81cf-4369-a11b-99f38748bf43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[23.60933423, 46.93043551],\n",
+       "        [23.90711768, 46.88691987],\n",
+       "        [23.79700427, 47.16261748],\n",
+       "        ...,\n",
+       "        [23.032924  , 47.074191  ],\n",
+       "        [23.49914425, 47.63647085],\n",
+       "        [24.25883819, 46.84755526]],\n",
+       "\n",
+       "       [[23.33069528, 47.56639153],\n",
+       "        [23.09777527, 47.2146956 ],\n",
+       "        [23.05412125, 47.1411097 ],\n",
+       "        ...,\n",
+       "        [23.24953573, 46.97386375],\n",
+       "        [24.01176736, 47.94960743],\n",
+       "        [23.55236722, 47.13777923]],\n",
+       "\n",
+       "       [[23.79786639, 46.96386755],\n",
+       "        [23.46444472, 47.59128973],\n",
+       "        [22.8140835 , 47.66585555],\n",
+       "        ...,\n",
+       "        [23.15080538, 46.5439213 ],\n",
+       "        [23.48837911, 47.34697533],\n",
+       "        [23.72471432, 47.05021965]],\n",
+       "\n",
+       "       [[24.07620954, 47.69002909],\n",
+       "        [23.82403699, 47.13716894],\n",
+       "        [24.20716787, 46.69481069],\n",
+       "        ...,\n",
+       "        [23.1199847 , 47.66976025],\n",
+       "        [23.34873127, 47.77105025],\n",
+       "        [23.28655978, 47.18404127]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.33 19.92 20.24 ... 21.89 20.18</div><input id='attrs-042af49d-27f3-4221-830c-f6d5dd5fc80c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-042af49d-27f3-4221-830c-f6d5dd5fc80c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-409c9cc2-ecd9-4f9d-b8ca-20d6c44e76e3' class='xr-var-data-in' type='checkbox'><label for='data-409c9cc2-ecd9-4f9d-b8ca-20d6c44e76e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[19.33417841, 19.91955985, 20.23866497, ..., 21.80047097,\n",
+       "        20.59136967, 20.62122696],\n",
+       "       [20.0848882 , 21.46942372, 20.20988597, ..., 20.54573008,\n",
+       "        20.62447926, 22.32965653],\n",
+       "       [19.42372818, 20.73474846, 20.46166964, ..., 22.02000788,\n",
+       "        19.33905001, 21.52528099],\n",
+       "       [20.23178177, 20.25899728, 19.9772469 , ..., 21.54493759,\n",
+       "        21.8890317 , 20.17634517]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsilon</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9637 1.067 1.207 ... 0.9343 1.014</div><input id='attrs-01291eed-20a5-4729-a51d-14516c530d12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01291eed-20a5-4729-a51d-14516c530d12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a62b9a6-a327-4830-bf8a-c6b5d23b29a7' class='xr-var-data-in' type='checkbox'><label for='data-3a62b9a6-a327-4830-bf8a-c6b5d23b29a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.96366918, 1.06740457, 1.20677884, ..., 1.22784688, 1.15413438,\n",
+       "        1.19338816],\n",
+       "       [1.02883767, 1.09579653, 0.94224374, ..., 1.0018401 , 1.00563607,\n",
+       "        1.11652423],\n",
+       "       [1.09313392, 1.19263084, 1.12249412, ..., 1.41439128, 1.0006801 ,\n",
+       "        1.21720054],\n",
+       "       [1.12605103, 0.89736591, 1.09018178, ..., 0.83718189, 0.93430395,\n",
+       "        1.01405476]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk</span></div><div class='xr-var-dims'>(chain, draw, oid, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4094 0.3426 ... 2.435 -0.3743</div><input id='attrs-f4e7251e-959c-41ce-8657-5d27c98b5008' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4e7251e-959c-41ce-8657-5d27c98b5008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa00c5b4-cd84-422f-b513-4ce7892b7359' class='xr-var-data-in' type='checkbox'><label for='data-aa00c5b4-cd84-422f-b513-4ce7892b7359' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.33159371,  1.40040708],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.28655121],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.26374895,  1.41843967],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.3454739 ],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.67356704,  1.23985297],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.29837661],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [-0.25651817,  1.63587584],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.29287095],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [-0.23106533,  1.66683913],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.34150705],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc6b7227-c7dd-4559-9047-42fb6a808a22' class='xr-section-summary-in' type='checkbox'  ><label for='section-fc6b7227-c7dd-4559-9047-42fb6a808a22' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4488e3bf-8ed6-4037-a9a8-644f7f1bda29' class='xr-index-data-in' type='checkbox'/><label for='index-4488e3bf-8ed6-4037-a9a8-644f7f1bda29' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-19bd91b0-038d-436e-ac2a-9fc25de51b58' class='xr-index-data-in' type='checkbox'/><label for='index-19bd91b0-038d-436e-ac2a-9fc25de51b58' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a80943c9-9a7c-4ea6-99ab-b8a1fd95f37b' class='xr-index-data-in' type='checkbox'/><label for='index-a80943c9-9a7c-4ea6-99ab-b8a1fd95f37b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xk_nm&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_unobserved_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fa8a1f61-b36b-4090-8177-2c757e5c7602' class='xr-index-data-in' type='checkbox'/><label for='index-fa8a1f61-b36b-4090-8177-2c757e5c7602' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;xk_unobserved_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xj_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6408be23-2266-480a-8659-fcf52dadef65' class='xr-index-data-in' type='checkbox'/><label for='index-6408be23-2266-480a-8659-fcf52dadef65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xj_nm&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oid</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-36bdfd2d-4dfd-475b-92f7-4edcad640ea3' class='xr-index-data-in' type='checkbox'/><label for='index-36bdfd2d-4dfd-475b-92f7-4edcad640ea3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;o01&#x27;, &#x27;o02&#x27;, &#x27;o03&#x27;, &#x27;o04&#x27;, &#x27;o06&#x27;, &#x27;o07&#x27;, &#x27;o08&#x27;, &#x27;o09&#x27;, &#x27;o10&#x27;, &#x27;o12&#x27;,\n",
+       "       &#x27;o13&#x27;, &#x27;o14&#x27;, &#x27;o15&#x27;, &#x27;o16&#x27;, &#x27;o17&#x27;, &#x27;o18&#x27;, &#x27;o21&#x27;, &#x27;o22&#x27;, &#x27;o23&#x27;, &#x27;o24&#x27;,\n",
+       "       &#x27;o27&#x27;, &#x27;o28&#x27;, &#x27;o30&#x27;, &#x27;o31&#x27;, &#x27;o32&#x27;, &#x27;o33&#x27;, &#x27;o34&#x27;, &#x27;o36&#x27;, &#x27;o37&#x27;, &#x27;o38&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;oid&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bcccc62-bf06-450e-a8ea-3435ab6d45f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bcccc62-bf06-450e-a8ea-3435ab6d45f6' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-16T10:46:11.401365+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>181.2683072090149</dd><dt><span>tuning_steps :</span></dt><dd>3000</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ida_h = deepcopy(ida)\n",
+    "# leave only posterior\n",
+    "del (\n",
+    "    ida_h.log_likelihood,\n",
+    "    ida_h.sample_stats,\n",
+    "    ida_h.prior,\n",
+    "    ida_h.prior_predictive,\n",
+    "    ida_h.posterior_predictive,\n",
+    "    ida_h.observed_data,\n",
+    "    ida_h.constant_data,\n",
+    ")\n",
+    "ida_h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [xk_observed, xk_unobserved]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ee055da7a26430495d1d0bfb8904db6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "              <div class='xr-header'>\n",
+       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
+       "              </div>\n",
+       "              <ul class=\"xr-sections group-sections\">\n",
+       "              \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_posterior4fc4adea-1be3-4b33-bc43-db78302863d7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior4fc4adea-1be3-4b33-bc43-db78302863d7\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1MB\n",
+       "Dimensions:              (chain: 4, draw: 500, xk_nm: 2,\n",
+       "                          xk_unobserved_dim_0: 24, xj_nm: 3, oid: 30)\n",
+       "Coordinates:\n",
+       "  * chain                (chain) int64 32B 0 1 2 3\n",
+       "  * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
+       "  * xk_nm                (xk_nm) &lt;U1 8B &#x27;c&#x27; &#x27;d&#x27;\n",
+       "  * xk_unobserved_dim_0  (xk_unobserved_dim_0) int64 192B 0 1 2 3 ... 21 22 23\n",
+       "  * xj_nm                (xj_nm) &lt;U9 108B &#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;\n",
+       "  * oid                  (oid) &lt;U3 360B &#x27;o01&#x27; &#x27;o02&#x27; &#x27;o03&#x27; ... &#x27;o36&#x27; &#x27;o37&#x27; &#x27;o38&#x27;\n",
+       "Data variables:\n",
+       "    xk_mu                (chain, draw, xk_nm) float64 32kB 0.2089 ... 0.2972\n",
+       "    xk_unobserved        (chain, draw, xk_unobserved_dim_0) float64 384kB 0.3...\n",
+       "    beta_j               (chain, draw, xj_nm) float64 48kB 280.5 ... -55.48\n",
+       "    beta_k               (chain, draw, xk_nm) float64 32kB 23.61 46.93 ... 47.18\n",
+       "    beta_sigma           (chain, draw) float64 16kB 19.33 19.92 ... 21.89 20.18\n",
+       "    epsilon              (chain, draw) float64 16kB 0.9637 1.067 ... 1.014\n",
+       "    xk                   (chain, draw, oid, xk_nm) float64 960kB -0.4094 ... ...\n",
+       "Attributes:\n",
+       "    created_at:                 2024-12-16T10:46:11.401365+00:00\n",
+       "    arviz_version:              0.20.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2\n",
+       "    sampling_time:              181.2683072090149\n",
+       "    tuning_steps:               3000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-917ed368-e2cc-47f1-86f8-6d2e07f3b3ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-917ed368-e2cc-47f1-86f8-6d2e07f3b3ed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>xk_nm</span>: 2</li><li><span class='xr-has-index'>xk_unobserved_dim_0</span>: 24</li><li><span class='xr-has-index'>xj_nm</span>: 3</li><li><span class='xr-has-index'>oid</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-23d441dc-7579-452c-8816-fceb115a3116' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23d441dc-7579-452c-8816-fceb115a3116' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-388d0938-150b-42d1-aba5-5de35d9ce888' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-388d0938-150b-42d1-aba5-5de35d9ce888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ef895c-0c93-4835-90b8-bb01c132dbf0' class='xr-var-data-in' type='checkbox'><label for='data-c0ef895c-0c93-4835-90b8-bb01c132dbf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-5635c61f-0a14-4945-b4ec-ca64cc5e1acc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5635c61f-0a14-4945-b4ec-ca64cc5e1acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a6558e8-3ed2-4186-85a7-be2276744b6f' class='xr-var-data-in' type='checkbox'><label for='data-3a6558e8-3ed2-4186-85a7-be2276744b6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_nm</span></div><div class='xr-var-dims'>(xk_nm)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;c&#x27; &#x27;d&#x27;</div><input id='attrs-bf78502f-e240-4582-84b0-a19511fe42ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf78502f-e240-4582-84b0-a19511fe42ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ff3aa3-0d75-40c6-b973-06708cb128c5' class='xr-var-data-in' type='checkbox'><label for='data-92ff3aa3-0d75-40c6-b973-06708cb128c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_unobserved_dim_0</span></div><div class='xr-var-dims'>(xk_unobserved_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-bc38ce33-6042-4a7f-aa5d-bd164926bcbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc38ce33-6042-4a7f-aa5d-bd164926bcbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3280b177-8731-447d-bb56-634faf58a54e' class='xr-var-data-in' type='checkbox'><label for='data-3280b177-8731-447d-bb56-634faf58a54e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xj_nm</span></div><div class='xr-var-dims'>(xj_nm)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;</div><input id='attrs-75338fb6-462e-4073-9f10-99796e7f5701' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75338fb6-462e-4073-9f10-99796e7f5701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c9a80c-fe72-4676-9e73-88b948c66d91' class='xr-var-data-in' type='checkbox'><label for='data-d5c9a80c-fe72-4676-9e73-88b948c66d91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oid</span></div><div class='xr-var-dims'>(oid)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;o01&#x27; &#x27;o02&#x27; &#x27;o03&#x27; ... &#x27;o37&#x27; &#x27;o38&#x27;</div><input id='attrs-16a433ce-edd3-4f6d-be96-64a3f7e7468b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16a433ce-edd3-4f6d-be96-64a3f7e7468b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5d3c468-029f-4e7c-a944-82d904fdb899' class='xr-var-data-in' type='checkbox'><label for='data-c5d3c468-029f-4e7c-a944-82d904fdb899' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;o01&#x27;, &#x27;o02&#x27;, &#x27;o03&#x27;, &#x27;o04&#x27;, &#x27;o06&#x27;, &#x27;o07&#x27;, &#x27;o08&#x27;, &#x27;o09&#x27;, &#x27;o10&#x27;, &#x27;o12&#x27;,\n",
+       "       &#x27;o13&#x27;, &#x27;o14&#x27;, &#x27;o15&#x27;, &#x27;o16&#x27;, &#x27;o17&#x27;, &#x27;o18&#x27;, &#x27;o21&#x27;, &#x27;o22&#x27;, &#x27;o23&#x27;, &#x27;o24&#x27;,\n",
+       "       &#x27;o27&#x27;, &#x27;o28&#x27;, &#x27;o30&#x27;, &#x27;o31&#x27;, &#x27;o32&#x27;, &#x27;o33&#x27;, &#x27;o34&#x27;, &#x27;o36&#x27;, &#x27;o37&#x27;, &#x27;o38&#x27;],\n",
+       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07516737-bd7b-4cfc-965b-291acb1cdfd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07516737-bd7b-4cfc-965b-291acb1cdfd0' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xk_mu</span></div><div class='xr-var-dims'>(chain, draw, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2089 0.1731 ... -0.2048 0.2972</div><input id='attrs-6e02a2c6-a530-4757-b409-8a9c01685f3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e02a2c6-a530-4757-b409-8a9c01685f3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd35182d-21df-4439-b186-e6dcb887880c' class='xr-var-data-in' type='checkbox'><label for='data-cd35182d-21df-4439-b186-e6dcb887880c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.20891809,  0.17313158],\n",
+       "        [ 0.10968446, -0.07713499],\n",
+       "        [ 0.34814158,  0.14390001],\n",
+       "        ...,\n",
+       "        [ 0.1040242 ,  0.0791114 ],\n",
+       "        [ 0.46729255,  0.01320396],\n",
+       "        [-0.18576857,  0.00399746]],\n",
+       "\n",
+       "       [[ 0.3353833 , -0.06487559],\n",
+       "        [-0.01418716,  0.19377117],\n",
+       "        [ 0.27807151, -0.02909548],\n",
+       "        ...,\n",
+       "        [ 0.0043824 ,  0.27432789],\n",
+       "        [ 0.08791309, -0.04719615],\n",
+       "        [-0.13119566,  0.29685589]],\n",
+       "\n",
+       "       [[ 0.25890938,  0.13817301],\n",
+       "        [-0.21112456,  0.50364429],\n",
+       "        [ 0.17563658, -0.23627762],\n",
+       "        ...,\n",
+       "        [-0.09267719,  0.13752434],\n",
+       "        [ 0.27621564,  0.07859173],\n",
+       "        [-0.21930505,  0.08161181]],\n",
+       "\n",
+       "       [[ 0.30239542,  0.09277564],\n",
+       "        [ 0.57037304,  0.17265029],\n",
+       "        [ 0.73818484,  0.0257427 ],\n",
+       "        ...,\n",
+       "        [-0.0413971 ,  0.12331163],\n",
+       "        [ 0.1627879 , -0.01322874],\n",
+       "        [-0.20475786,  0.29720492]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk_unobserved</span></div><div class='xr-var-dims'>(chain, draw, xk_unobserved_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3316 1.4 -1.065 ... 0.698 0.3415</div><input id='attrs-ae4f8156-211b-4694-9743-8091477883a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae4f8156-211b-4694-9743-8091477883a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5475486-ddf4-49ff-bd6b-499d365019e3' class='xr-var-data-in' type='checkbox'><label for='data-d5475486-ddf4-49ff-bd6b-499d365019e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.33159371,  1.40040708, -1.0653712 , ..., -0.53268713,\n",
+       "          0.63074055,  0.28655121],\n",
+       "        [ 0.26374895,  1.41843967, -0.96178183, ..., -1.33689741,\n",
+       "          1.01024666,  0.3454739 ],\n",
+       "        [ 0.67356704,  1.23985297, -1.1073857 , ...,  0.09362308,\n",
+       "          0.30041517,  0.31594624],\n",
+       "        ...,\n",
+       "        [ 1.50794207,  0.80548865, -1.03550396, ...,  0.56284223,\n",
+       "          0.03194101,  0.34082027],\n",
+       "        [ 0.99117336,  1.07671813, -1.058888  , ...,  0.81190703,\n",
+       "         -0.03141332,  0.31834347],\n",
+       "        [-0.43352998,  1.80813609, -1.0410773 , ...,  0.22429163,\n",
+       "          0.27525897,  0.27741251]],\n",
+       "\n",
+       "       [[ 1.67037953,  0.72094343, -0.95802624, ..., -0.86644935,\n",
+       "          0.71773119,  0.31022637],\n",
+       "        [-0.10049297,  1.5951994 , -1.08270023, ..., -0.36839778,\n",
+       "          0.50771374,  0.3173316 ],\n",
+       "        [-0.87007526,  1.99752237, -1.09399038, ...,  0.51241705,\n",
+       "          0.09381582,  0.30601479],\n",
+       "...\n",
+       "        [-0.93134138,  2.04282757, -1.11705007, ...,  1.49719712,\n",
+       "         -0.33292181,  0.33624381],\n",
+       "        [ 1.4983631 ,  0.82001405, -1.10844119, ..., -1.34678171,\n",
+       "          1.04991977,  0.29547717],\n",
+       "        [ 0.10682445,  1.52713878, -0.93040434, ..., -0.58400882,\n",
+       "          0.61899806,  0.32282628]],\n",
+       "\n",
+       "       [[ 1.3814937 ,  0.82372824, -0.97929634, ...,  1.7397712 ,\n",
+       "         -0.55371778,  0.31179638],\n",
+       "        [ 0.1607087 ,  1.45434902, -1.063863  , ...,  1.5941148 ,\n",
+       "         -0.45603364,  0.28632649],\n",
+       "        [ 0.44440169,  1.35847376, -1.03721039, ...,  0.91956431,\n",
+       "         -0.15502685,  0.30801239],\n",
+       "        ...,\n",
+       "        [ 0.37593545,  1.32873593, -1.00345814, ...,  1.0778376 ,\n",
+       "         -0.20802916,  0.29837661],\n",
+       "        [-0.25651817,  1.63587584, -1.06096069, ...,  0.98297266,\n",
+       "         -0.16731735,  0.29287095],\n",
+       "        [-0.23106533,  1.66683913, -1.13464076, ..., -0.72624689,\n",
+       "          0.69801981,  0.34150705]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_j</span></div><div class='xr-var-dims'>(chain, draw, xj_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>280.5 7.957 -56.4 ... 8.096 -55.48</div><input id='attrs-8b90c003-e42a-4f2f-9b65-11b7aa880e24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b90c003-e42a-4f2f-9b65-11b7aa880e24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c02966e-4574-4a88-8c7f-af5d293a8929' class='xr-var-data-in' type='checkbox'><label for='data-7c02966e-4574-4a88-8c7f-af5d293a8929' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[280.50156619,   7.95655647, -56.40466968],\n",
+       "        [281.30548198,   8.52646745, -56.04352876],\n",
+       "        [280.57953457,   8.34469974, -55.74007472],\n",
+       "        ...,\n",
+       "        [280.68720187,   8.99938731, -55.71880658],\n",
+       "        [280.44660017,   8.67757231, -56.18057113],\n",
+       "        [281.23834165,   7.85951359, -56.45009634]],\n",
+       "\n",
+       "       [[280.74688301,   8.8760765 , -55.73783306],\n",
+       "        [281.35766155,   8.54959605, -55.72214807],\n",
+       "        [281.09571944,   8.0983401 , -56.21042462],\n",
+       "        ...,\n",
+       "        [280.78619266,   8.05854668, -55.29527511],\n",
+       "        [281.1566593 ,   8.6553639 , -56.27352171],\n",
+       "        [280.5536389 ,   9.36058137, -54.85382783]],\n",
+       "\n",
+       "       [[281.74166633,   8.04564302, -56.43724676],\n",
+       "        [281.59886996,   8.52009945, -55.98341478],\n",
+       "        [281.01564187,   8.26401633, -55.62654631],\n",
+       "        ...,\n",
+       "        [281.20781066,   8.48541819, -55.72847547],\n",
+       "        [281.49408148,   8.26841815, -55.92252308],\n",
+       "        [280.37885761,   8.70555729, -55.86262397]],\n",
+       "\n",
+       "       [[281.03786928,   8.6668038 , -56.68272186],\n",
+       "        [281.11264743,   8.4689861 , -55.61194155],\n",
+       "        [281.0122357 ,   8.66553863, -56.10891835],\n",
+       "        ...,\n",
+       "        [281.4803094 ,   8.81745181, -55.40687754],\n",
+       "        [281.41787839,   8.97202468, -55.74203196],\n",
+       "        [280.66765598,   8.09628328, -55.48191212]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_k</span></div><div class='xr-var-dims'>(chain, draw, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.61 46.93 23.91 ... 23.29 47.18</div><input id='attrs-c0d1f255-735b-4324-8940-ad6d52b95efe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0d1f255-735b-4324-8940-ad6d52b95efe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f28b66d4-8d65-486a-bb7b-9b443abe0b2f' class='xr-var-data-in' type='checkbox'><label for='data-f28b66d4-8d65-486a-bb7b-9b443abe0b2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[23.60933423, 46.93043551],\n",
+       "        [23.90711768, 46.88691987],\n",
+       "        [23.79700427, 47.16261748],\n",
+       "        ...,\n",
+       "        [23.032924  , 47.074191  ],\n",
+       "        [23.49914425, 47.63647085],\n",
+       "        [24.25883819, 46.84755526]],\n",
+       "\n",
+       "       [[23.33069528, 47.56639153],\n",
+       "        [23.09777527, 47.2146956 ],\n",
+       "        [23.05412125, 47.1411097 ],\n",
+       "        ...,\n",
+       "        [23.24953573, 46.97386375],\n",
+       "        [24.01176736, 47.94960743],\n",
+       "        [23.55236722, 47.13777923]],\n",
+       "\n",
+       "       [[23.79786639, 46.96386755],\n",
+       "        [23.46444472, 47.59128973],\n",
+       "        [22.8140835 , 47.66585555],\n",
+       "        ...,\n",
+       "        [23.15080538, 46.5439213 ],\n",
+       "        [23.48837911, 47.34697533],\n",
+       "        [23.72471432, 47.05021965]],\n",
+       "\n",
+       "       [[24.07620954, 47.69002909],\n",
+       "        [23.82403699, 47.13716894],\n",
+       "        [24.20716787, 46.69481069],\n",
+       "        ...,\n",
+       "        [23.1199847 , 47.66976025],\n",
+       "        [23.34873127, 47.77105025],\n",
+       "        [23.28655978, 47.18404127]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.33 19.92 20.24 ... 21.89 20.18</div><input id='attrs-69403ba9-f388-44f7-91dc-897835132c26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69403ba9-f388-44f7-91dc-897835132c26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de5b969-ae28-4477-9ed6-628a97d3bb8b' class='xr-var-data-in' type='checkbox'><label for='data-9de5b969-ae28-4477-9ed6-628a97d3bb8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[19.33417841, 19.91955985, 20.23866497, ..., 21.80047097,\n",
+       "        20.59136967, 20.62122696],\n",
+       "       [20.0848882 , 21.46942372, 20.20988597, ..., 20.54573008,\n",
+       "        20.62447926, 22.32965653],\n",
+       "       [19.42372818, 20.73474846, 20.46166964, ..., 22.02000788,\n",
+       "        19.33905001, 21.52528099],\n",
+       "       [20.23178177, 20.25899728, 19.9772469 , ..., 21.54493759,\n",
+       "        21.8890317 , 20.17634517]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsilon</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9637 1.067 1.207 ... 0.9343 1.014</div><input id='attrs-5ae9dc03-6031-49a3-8399-86c8e41c5d9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ae9dc03-6031-49a3-8399-86c8e41c5d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5d42f1-7400-4bdf-9435-cb1d433ef432' class='xr-var-data-in' type='checkbox'><label for='data-9f5d42f1-7400-4bdf-9435-cb1d433ef432' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.96366918, 1.06740457, 1.20677884, ..., 1.22784688, 1.15413438,\n",
+       "        1.19338816],\n",
+       "       [1.02883767, 1.09579653, 0.94224374, ..., 1.0018401 , 1.00563607,\n",
+       "        1.11652423],\n",
+       "       [1.09313392, 1.19263084, 1.12249412, ..., 1.41439128, 1.0006801 ,\n",
+       "        1.21720054],\n",
+       "       [1.12605103, 0.89736591, 1.09018178, ..., 0.83718189, 0.93430395,\n",
+       "        1.01405476]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk</span></div><div class='xr-var-dims'>(chain, draw, oid, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4094 0.3426 ... 2.435 -0.3743</div><input id='attrs-449d654d-6885-495a-86aa-057f34dad80a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-449d654d-6885-495a-86aa-057f34dad80a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daea2ca3-1738-41f1-b25e-7544d8922bc7' class='xr-var-data-in' type='checkbox'><label for='data-daea2ca3-1738-41f1-b25e-7544d8922bc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.33159371,  1.40040708],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.28655121],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.26374895,  1.41843967],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.3454739 ],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [ 0.67356704,  1.23985297],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.29837661],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [-0.25651817,  1.63587584],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.29287095],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]],\n",
+       "\n",
+       "        [[-0.40942107,  0.34258686],\n",
+       "         [-0.03386835,  0.38560622],\n",
+       "         [-0.23106533,  1.66683913],\n",
+       "         ...,\n",
+       "         [ 0.71470619,  0.34150705],\n",
+       "         [ 0.4497917 ,  1.19139863],\n",
+       "         [ 2.43507501, -0.37428327]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04f026b9-f91a-4fdc-bd98-9ef812e714f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-04f026b9-f91a-4fdc-bd98-9ef812e714f8' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-863f79b4-a95d-4916-ab97-ff9ca028aba5' class='xr-index-data-in' type='checkbox'/><label for='index-863f79b4-a95d-4916-ab97-ff9ca028aba5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-768b760d-0504-4db8-a14c-9a1c02bbc723' class='xr-index-data-in' type='checkbox'/><label for='index-768b760d-0504-4db8-a14c-9a1c02bbc723' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2f52df85-4122-4ae0-8648-53c1dec17b2c' class='xr-index-data-in' type='checkbox'/><label for='index-2f52df85-4122-4ae0-8648-53c1dec17b2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xk_nm&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_unobserved_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-faac7326-08e4-41d7-baa3-808ff560f72a' class='xr-index-data-in' type='checkbox'/><label for='index-faac7326-08e4-41d7-baa3-808ff560f72a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;xk_unobserved_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xj_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2162a38a-b2a2-4bf8-a76f-3a2f474b1077' class='xr-index-data-in' type='checkbox'/><label for='index-2162a38a-b2a2-4bf8-a76f-3a2f474b1077' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xj_nm&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oid</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b6842ce3-6d97-41fc-a81e-06133265aa88' class='xr-index-data-in' type='checkbox'/><label for='index-b6842ce3-6d97-41fc-a81e-06133265aa88' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;o01&#x27;, &#x27;o02&#x27;, &#x27;o03&#x27;, &#x27;o04&#x27;, &#x27;o06&#x27;, &#x27;o07&#x27;, &#x27;o08&#x27;, &#x27;o09&#x27;, &#x27;o10&#x27;, &#x27;o12&#x27;,\n",
+       "       &#x27;o13&#x27;, &#x27;o14&#x27;, &#x27;o15&#x27;, &#x27;o16&#x27;, &#x27;o17&#x27;, &#x27;o18&#x27;, &#x27;o21&#x27;, &#x27;o22&#x27;, &#x27;o23&#x27;, &#x27;o24&#x27;,\n",
+       "       &#x27;o27&#x27;, &#x27;o28&#x27;, &#x27;o30&#x27;, &#x27;o31&#x27;, &#x27;o32&#x27;, &#x27;o33&#x27;, &#x27;o34&#x27;, &#x27;o36&#x27;, &#x27;o37&#x27;, &#x27;o38&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;oid&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fef96021-7a29-4f47-8a74-58faea592187' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fef96021-7a29-4f47-8a74-58faea592187' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-16T10:46:11.401365+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>181.2683072090149</dd><dt><span>tuning_steps :</span></dt><dd>3000</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_posterior_predictivec2a3a9c7-f0cf-4c01-a891-504615a34b27\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior_predictivec2a3a9c7-f0cf-4c01-a891-504615a34b27\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 452kB\n",
+       "Dimensions:              (chain: 4, draw: 500, xk_unobserved_dim_2: 8, oid: 10,\n",
+       "                          xk_nm: 2)\n",
+       "Coordinates:\n",
+       "  * chain                (chain) int64 32B 0 1 2 3\n",
+       "  * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
+       "  * xk_unobserved_dim_2  (xk_unobserved_dim_2) int64 64B 0 1 2 3 4 5 6 7\n",
+       "  * oid                  (oid) &lt;U3 120B &#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; ... &#x27;o29&#x27; &#x27;o35&#x27; &#x27;o39&#x27;\n",
+       "  * xk_nm                (xk_nm) &lt;U1 8B &#x27;c&#x27; &#x27;d&#x27;\n",
+       "Data variables:\n",
+       "    xk_unobserved        (chain, draw, xk_unobserved_dim_2) float64 128kB 0.1...\n",
+       "    xk                   (chain, draw, oid, xk_nm) float64 320kB 0.1384 ... 1...\n",
+       "Attributes:\n",
+       "    created_at:                 2024-12-16T10:46:21.721655+00:00\n",
+       "    arviz_version:              0.20.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3141ea49-09be-4392-9071-5c16733251ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3141ea49-09be-4392-9071-5c16733251ac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>xk_unobserved_dim_2</span>: 8</li><li><span class='xr-has-index'>oid</span>: 10</li><li><span class='xr-has-index'>xk_nm</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-698e112c-e652-44e8-b076-00cc909fe209' class='xr-section-summary-in' type='checkbox'  checked><label for='section-698e112c-e652-44e8-b076-00cc909fe209' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-154874d2-eba0-4bbd-8f8d-aa081c036392' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-154874d2-eba0-4bbd-8f8d-aa081c036392' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94bb8be2-4694-4dc6-83ab-543cca074e7e' class='xr-var-data-in' type='checkbox'><label for='data-94bb8be2-4694-4dc6-83ab-543cca074e7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-dccbfa3b-b8f6-4b83-9e44-66666826ab80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dccbfa3b-b8f6-4b83-9e44-66666826ab80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-502e6482-9559-429d-af4e-471f163544cb' class='xr-var-data-in' type='checkbox'><label for='data-502e6482-9559-429d-af4e-471f163544cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_unobserved_dim_2</span></div><div class='xr-var-dims'>(xk_unobserved_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-ea02c864-7110-4c8c-9687-e7a077aed68b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea02c864-7110-4c8c-9687-e7a077aed68b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b4fe6d7-2b8f-41d6-b90f-e86557d8b029' class='xr-var-data-in' type='checkbox'><label for='data-0b4fe6d7-2b8f-41d6-b90f-e86557d8b029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oid</span></div><div class='xr-var-dims'>(oid)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; ... &#x27;o35&#x27; &#x27;o39&#x27;</div><input id='attrs-ae8c5c1b-7af9-4f05-a0b6-4d945ddb37ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae8c5c1b-7af9-4f05-a0b6-4d945ddb37ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ce9711-6ba7-49c5-8098-efe3104852af' class='xr-var-data-in' type='checkbox'><label for='data-38ce9711-6ba7-49c5-8098-efe3104852af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;],\n",
+       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_nm</span></div><div class='xr-var-dims'>(xk_nm)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;c&#x27; &#x27;d&#x27;</div><input id='attrs-48ea34a6-496a-476e-8d17-4281b5d6dd3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48ea34a6-496a-476e-8d17-4281b5d6dd3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e72b715-0ef3-4afd-b131-2f3ac7d97e56' class='xr-var-data-in' type='checkbox'><label for='data-9e72b715-0ef3-4afd-b131-2f3ac7d97e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c3b1107-b9fa-4fa2-9817-270ad6099ac2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c3b1107-b9fa-4fa2-9817-270ad6099ac2' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xk_unobserved</span></div><div class='xr-var-dims'>(chain, draw, xk_unobserved_dim_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1384 -0.1716 ... -1.115 -0.7012</div><input id='attrs-2b15b222-ca27-4200-94a1-ff9e3f9df667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b15b222-ca27-4200-94a1-ff9e3f9df667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e080c33-638b-4e5b-a471-cbb987ffb6df' class='xr-var-data-in' type='checkbox'><label for='data-6e080c33-638b-4e5b-a471-cbb987ffb6df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.38398970e-01, -1.71572202e-01,  9.63693195e-01, ...,\n",
+       "          1.68791563e+00,  2.61102907e-01, -1.90084182e+00],\n",
+       "        [-1.16834250e+00, -1.04426550e+00,  9.55005715e-01, ...,\n",
+       "         -5.66382826e-01, -7.29909137e-01,  2.47932387e-01],\n",
+       "        [-3.02794495e-01, -2.37152057e-01, -2.23398939e-01, ...,\n",
+       "         -1.15604406e+00,  1.53465034e+00, -1.20672250e+00],\n",
+       "        ...,\n",
+       "        [-1.26126266e+00,  7.04590350e-02, -1.22796267e-01, ...,\n",
+       "          8.00979661e-01, -2.26558969e-01, -2.86844591e-01],\n",
+       "        [-8.77953857e-02,  5.16448770e-01,  7.23671853e-01, ...,\n",
+       "          1.47188219e-01,  1.20559659e+00,  3.51890847e-02],\n",
+       "        [-1.06249114e+00,  6.83808730e-01, -1.16687178e+00, ...,\n",
+       "          1.50230325e+00, -1.75591313e+00, -1.22613485e+00]],\n",
+       "\n",
+       "       [[ 6.25708404e-01, -1.14982635e+00,  4.23263558e-01, ...,\n",
+       "          8.40265981e-01, -1.02747860e+00, -1.82678111e-01],\n",
+       "        [-2.31355932e-01, -5.85889672e-01, -1.00789257e+00, ...,\n",
+       "          9.52933579e-02,  6.91571572e-01, -7.74877324e-01],\n",
+       "        [-6.20824447e-01,  1.13070077e-02, -1.37254121e+00, ...,\n",
+       "          1.37938689e+00,  1.06110374e+00,  4.61172804e-01],\n",
+       "...\n",
+       "        [-3.02078576e-01, -1.88213313e-01,  8.17596888e-01, ...,\n",
+       "         -5.84433439e-01,  4.81188194e-01, -4.71785663e-01],\n",
+       "        [ 7.55166533e-01,  8.01897841e-01,  4.75626737e-01, ...,\n",
+       "          1.33228868e+00, -6.37998166e-02, -1.84531669e-01],\n",
+       "        [-1.23179172e+00, -2.21936013e-01,  2.04777706e-01, ...,\n",
+       "          1.31857556e-01,  6.55100450e-01, -1.83668264e+00]],\n",
+       "\n",
+       "       [[-7.22967767e-01, -1.76971571e+00,  1.42327051e+00, ...,\n",
+       "          2.25718129e-03,  4.04621318e-01, -1.07598447e+00],\n",
+       "        [ 1.94360925e+00, -3.16118802e-01,  1.99686729e+00, ...,\n",
+       "          1.04826266e+00, -1.43272236e-01,  4.90762781e-01],\n",
+       "        [-5.46353580e-01, -6.09092372e-01, -6.50878382e-01, ...,\n",
+       "         -7.10435821e-01,  4.80061194e-02,  1.47524742e+00],\n",
+       "        ...,\n",
+       "        [-1.73257446e-01, -7.84280071e-02,  7.87059969e-01, ...,\n",
+       "         -9.60744471e-01,  2.17777332e+00, -1.34735336e+00],\n",
+       "        [ 4.02375740e-01,  8.00217343e-01,  8.13724984e-01, ...,\n",
+       "          2.11059575e+00,  8.88030645e-01,  1.76982235e-01],\n",
+       "        [-5.75419207e-01, -2.43547991e+00, -2.23312275e+00, ...,\n",
+       "         -6.04373521e-01, -1.11483187e+00, -7.01150792e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk</span></div><div class='xr-var-dims'>(chain, draw, oid, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1384 -0.1716 ... -0.7012 1.527</div><input id='attrs-8d884b77-1673-45b4-9791-27e2cfc45784' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d884b77-1673-45b4-9791-27e2cfc45784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e887485e-77f5-4beb-ae71-c9531a6d4eae' class='xr-var-data-in' type='checkbox'><label for='data-e887485e-77f5-4beb-ae71-c9531a6d4eae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 1.38398970e-01, -1.71572202e-01],\n",
+       "         [-1.52722766e-01,  7.43524554e-01],\n",
+       "         [ 1.02847413e+00, -8.27321404e-01],\n",
+       "         ...,\n",
+       "         [ 1.68791563e+00,  2.54917206e-01],\n",
+       "         [-6.76628251e-01,  2.61102907e-01],\n",
+       "         [-1.90084182e+00, -3.23206696e-01]],\n",
+       "\n",
+       "        [[-1.16834250e+00, -1.04426550e+00],\n",
+       "         [-1.64154856e+00, -1.26593499e+00],\n",
+       "         [ 3.34732595e-01,  1.63890212e-01],\n",
+       "         ...,\n",
+       "         [-5.66382826e-01,  1.03644477e-01],\n",
+       "         [ 1.47342455e+00, -7.29909137e-01],\n",
+       "         [ 2.47932387e-01, -4.05472802e-01]],\n",
+       "\n",
+       "        [[-3.02794495e-01, -2.37152057e-01],\n",
+       "         [ 1.71188153e+00,  1.18024281e+00],\n",
+       "         [ 1.53628776e+00,  1.28461333e+00],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [-9.60744471e-01,  5.94168818e-01],\n",
+       "         [-3.24801330e-01,  2.17777332e+00],\n",
+       "         [-1.34735336e+00,  2.46251640e+00]],\n",
+       "\n",
+       "        [[ 4.02375740e-01,  8.00217343e-01],\n",
+       "         [-1.78865226e+00, -2.89581598e-01],\n",
+       "         [-9.38267015e-01, -1.53151709e+00],\n",
+       "         ...,\n",
+       "         [ 2.11059575e+00, -8.97034041e-01],\n",
+       "         [ 1.13768327e-01,  8.88030645e-01],\n",
+       "         [ 1.76982235e-01,  7.10247917e-01]],\n",
+       "\n",
+       "        [[-5.75419207e-01, -2.43547991e+00],\n",
+       "         [ 1.49194568e+00,  6.42474940e-01],\n",
+       "         [ 5.69475854e-01,  2.92061503e-01],\n",
+       "         ...,\n",
+       "         [-6.04373521e-01,  1.51540875e+00],\n",
+       "         [-4.08433291e-01, -1.11483187e+00],\n",
+       "         [-7.01150792e-01,  1.52666248e+00]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-642c7d31-1d63-45ff-aadf-c7da44dbdc5f' class='xr-section-summary-in' type='checkbox'  ><label for='section-642c7d31-1d63-45ff-aadf-c7da44dbdc5f' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-af21bbed-dad3-4bd9-87aa-321b0bebbd07' class='xr-index-data-in' type='checkbox'/><label for='index-af21bbed-dad3-4bd9-87aa-321b0bebbd07' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f2b943e1-3161-47f4-b20d-532f050b4775' class='xr-index-data-in' type='checkbox'/><label for='index-f2b943e1-3161-47f4-b20d-532f050b4775' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_unobserved_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1f02a9c1-b92c-4097-96bf-369eb7a2442b' class='xr-index-data-in' type='checkbox'/><label for='index-1f02a9c1-b92c-4097-96bf-369eb7a2442b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;xk_unobserved_dim_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oid</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-269b0ced-6ccd-4e6b-9345-6cf2fe712bd9' class='xr-index-data-in' type='checkbox'/><label for='index-269b0ced-6ccd-4e6b-9345-6cf2fe712bd9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;], dtype=&#x27;object&#x27;, name=&#x27;oid&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-66453c54-59f6-4d17-8564-7ac259b80706' class='xr-index-data-in' type='checkbox'/><label for='index-66453c54-59f6-4d17-8564-7ac259b80706' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xk_nm&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5142a326-85dc-43ea-85d1-00cb1752d52e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5142a326-85dc-43ea-85d1-00cb1752d52e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-16T10:46:21.721655+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior\n",
+       "\t> posterior_predictive"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NOTE for later clarity and to avoid overwriting, temporarily save posterior\n",
+    "# [xk, xk_unobserved] into group `posterior_predictive`` and remove any more\n",
+    "# unnecessary groups\n",
+    "\n",
+    "with mdla_h:\n",
+    "    ida_h.extend(\n",
+    "        pm.sample_posterior_predictive(\n",
+    "            trace=ida_h.posterior, var_names=[\"xk_unobserved\", \"xk\"], predictions=False\n",
+    "        ),\n",
+    "        join=\"right\",\n",
+    "    )\n",
+    "\n",
+    "del ida_h.observed_data, ida_h.constant_data\n",
+    "ida_h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5.3 Thirdly: sub the predictions for `xk_unobserved` into idata and sample PPC for `yhat`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [xk_observed, yhat]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6ac63466ddd40f68810e5e856415091",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "              <div class='xr-header'>\n",
+       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
+       "              </div>\n",
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+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
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+       "  margin-top: 0;\n",
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+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "  border: 2px solid var(--xr-font-color0);\n",
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+       "  font-weight: 500;\n",
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+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
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+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
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+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
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+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
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+       "\n",
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+       "  display: contents;\n",
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+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
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+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
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+       "\n",
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+       "\n",
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+       "\n",
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+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 612kB\n",
+       "Dimensions:              (chain: 4, draw: 500, oid: 10, xk_unobserved_dim_2: 8,\n",
+       "                          xk_nm: 2)\n",
+       "Coordinates:\n",
+       "  * chain                (chain) int64 32B 0 1 2 3\n",
+       "  * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499\n",
+       "  * oid                  (oid) &lt;U3 120B &#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; ... &#x27;o29&#x27; &#x27;o35&#x27; &#x27;o39&#x27;\n",
+       "  * xk_unobserved_dim_2  (xk_unobserved_dim_2) int64 64B 0 1 2 3 4 5 6 7\n",
+       "  * xk_nm                (xk_nm) &lt;U1 8B &#x27;c&#x27; &#x27;d&#x27;\n",
+       "Data variables:\n",
+       "    yhat                 (chain, draw, oid) float64 160kB 216.2 328.5 ... 357.7\n",
+       "    xk_unobserved        (chain, draw, xk_unobserved_dim_2) float64 128kB 0.1...\n",
+       "    xk                   (chain, draw, oid, xk_nm) float64 320kB 0.1384 ... 1...\n",
+       "Attributes:\n",
+       "    created_at:                 2024-12-16T10:46:22.072054+00:00\n",
+       "    arviz_version:              0.20.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b4de6e46-8dd0-4cac-8a30-a5bed70d9513' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b4de6e46-8dd0-4cac-8a30-a5bed70d9513' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>oid</span>: 10</li><li><span class='xr-has-index'>xk_unobserved_dim_2</span>: 8</li><li><span class='xr-has-index'>xk_nm</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-341b6b5c-6896-4e9d-8cd2-3232a8ef019e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-341b6b5c-6896-4e9d-8cd2-3232a8ef019e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-a7933004-45ab-4a15-a6f0-98d854ae3f99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7933004-45ab-4a15-a6f0-98d854ae3f99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eeea2e6-fccf-480c-a22e-0e6c869aee96' class='xr-var-data-in' type='checkbox'><label for='data-5eeea2e6-fccf-480c-a22e-0e6c869aee96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-30f22601-b97e-4b5a-b209-5058f6a88b55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30f22601-b97e-4b5a-b209-5058f6a88b55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcd8d7d3-dc78-4fb9-a30f-0350691756f8' class='xr-var-data-in' type='checkbox'><label for='data-fcd8d7d3-dc78-4fb9-a30f-0350691756f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oid</span></div><div class='xr-var-dims'>(oid)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; ... &#x27;o35&#x27; &#x27;o39&#x27;</div><input id='attrs-0718cf43-e6e9-4b63-b8d0-e520c9a48531' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0718cf43-e6e9-4b63-b8d0-e520c9a48531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84c9f020-4e78-427c-a906-c7cdfb57e583' class='xr-var-data-in' type='checkbox'><label for='data-84c9f020-4e78-427c-a906-c7cdfb57e583' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;],\n",
+       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_unobserved_dim_2</span></div><div class='xr-var-dims'>(xk_unobserved_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-e6d7ea90-cb9b-4782-825e-38134e579014' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6d7ea90-cb9b-4782-825e-38134e579014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6986433d-087b-4ff2-8207-b3bc3538c21f' class='xr-var-data-in' type='checkbox'><label for='data-6986433d-087b-4ff2-8207-b3bc3538c21f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xk_nm</span></div><div class='xr-var-dims'>(xk_nm)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;c&#x27; &#x27;d&#x27;</div><input id='attrs-941adcb6-67e6-4255-b8a5-fa68cd12fca1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-941adcb6-67e6-4255-b8a5-fa68cd12fca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e00af4b-2cce-481a-9908-e106aef450fc' class='xr-var-data-in' type='checkbox'><label for='data-0e00af4b-2cce-481a-9908-e106aef450fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87af4be5-05eb-4657-9555-0e64021081a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87af4be5-05eb-4657-9555-0e64021081a3' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>yhat</span></div><div class='xr-var-dims'>(chain, draw, oid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>216.2 328.5 339.1 ... 189.0 357.7</div><input id='attrs-a81366fd-8e31-405e-ab7c-53e2876f951a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a81366fd-8e31-405e-ab7c-53e2876f951a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbb5fbe6-446d-42b4-9270-254b3a48135b' class='xr-var-data-in' type='checkbox'><label for='data-cbb5fbe6-446d-42b4-9270-254b3a48135b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[216.18968091, 328.46936322, 339.13419039, ..., 365.71707499,\n",
+       "         207.40831193, 261.86217473],\n",
+       "        [145.09849479, 288.23636001, 191.41839255, ..., 245.45952939,\n",
+       "         220.71170018, 375.17411717],\n",
+       "        [201.79789443, 181.21097568, 207.10841085, ..., 321.58982457,\n",
+       "         295.63854771, 256.86967792],\n",
+       "        ...,\n",
+       "        [196.14165294, 321.54003789, 297.64288087, ..., 369.72592665,\n",
+       "         218.56380603, 313.44798754],\n",
+       "        [243.35937493, 248.60381152, 276.27726926, ..., 170.91386723,\n",
+       "         316.6719664 , 420.18610109],\n",
+       "        [227.84601965, 238.87371013, 184.15454819, ..., 336.2531988 ,\n",
+       "         129.95058166, 277.43043422]],\n",
+       "\n",
+       "       [[180.57568502, 260.72389667, 192.57473652, ..., 202.43047671,\n",
+       "         183.50633883, 325.2355352 ],\n",
+       "        [188.91267412, 216.67509958, 185.26476853, ..., 356.03589654,\n",
+       "         258.45611872, 343.8828342 ],\n",
+       "        [207.07928687, 269.56614233, 307.92137424, ..., 324.79867121,\n",
+       "         325.77863209, 253.36128158],\n",
+       "...\n",
+       "        [204.22603254, 273.72943507, 318.67818012, ..., 337.57764456,\n",
+       "         252.30095385, 210.88687848],\n",
+       "        [278.89649489, 237.64999933, 185.95987346, ..., 264.56347155,\n",
+       "         242.20413101, 339.85021355],\n",
+       "        [182.25596052, 299.50030675, 284.49285246, ..., 260.38144756,\n",
+       "         293.06358889, 228.66288103]],\n",
+       "\n",
+       "       [[119.2949809 , 256.02728706, 253.45693473, ..., 272.98394441,\n",
+       "         198.38900336, 257.59875737],\n",
+       "        [255.02744054, 332.71858575, 237.77203693, ..., 355.93905588,\n",
+       "         237.01249998, 231.865262  ],\n",
+       "        [183.90376019, 276.2513336 , 276.33999333, ..., 279.06929496,\n",
+       "         300.07204433, 323.12210115],\n",
+       "        ...,\n",
+       "        [215.64694936, 255.88705949, 228.41412702, ..., 230.05847156,\n",
+       "         360.85206452, 231.03881501],\n",
+       "        [270.71880806, 251.12357147, 285.06746288, ..., 279.31021204,\n",
+       "         281.41572014, 327.77844798],\n",
+       "        [ 93.79926124, 230.32700054, 289.25360317, ..., 213.06125508,\n",
+       "         188.98441974, 357.68111513]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk_unobserved</span></div><div class='xr-var-dims'>(chain, draw, xk_unobserved_dim_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1384 -0.1716 ... -1.115 -0.7012</div><input id='attrs-e8f1ed83-96ff-4b5b-a773-f28a954dd124' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8f1ed83-96ff-4b5b-a773-f28a954dd124' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6742aa59-2c8f-4059-b756-b0e2ef22f6b8' class='xr-var-data-in' type='checkbox'><label for='data-6742aa59-2c8f-4059-b756-b0e2ef22f6b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.38398970e-01, -1.71572202e-01,  9.63693195e-01, ...,\n",
+       "          1.68791563e+00,  2.61102907e-01, -1.90084182e+00],\n",
+       "        [-1.16834250e+00, -1.04426550e+00,  9.55005715e-01, ...,\n",
+       "         -5.66382826e-01, -7.29909137e-01,  2.47932387e-01],\n",
+       "        [-3.02794495e-01, -2.37152057e-01, -2.23398939e-01, ...,\n",
+       "         -1.15604406e+00,  1.53465034e+00, -1.20672250e+00],\n",
+       "        ...,\n",
+       "        [-1.26126266e+00,  7.04590350e-02, -1.22796267e-01, ...,\n",
+       "          8.00979661e-01, -2.26558969e-01, -2.86844591e-01],\n",
+       "        [-8.77953857e-02,  5.16448770e-01,  7.23671853e-01, ...,\n",
+       "          1.47188219e-01,  1.20559659e+00,  3.51890847e-02],\n",
+       "        [-1.06249114e+00,  6.83808730e-01, -1.16687178e+00, ...,\n",
+       "          1.50230325e+00, -1.75591313e+00, -1.22613485e+00]],\n",
+       "\n",
+       "       [[ 6.25708404e-01, -1.14982635e+00,  4.23263558e-01, ...,\n",
+       "          8.40265981e-01, -1.02747860e+00, -1.82678111e-01],\n",
+       "        [-2.31355932e-01, -5.85889672e-01, -1.00789257e+00, ...,\n",
+       "          9.52933579e-02,  6.91571572e-01, -7.74877324e-01],\n",
+       "        [-6.20824447e-01,  1.13070077e-02, -1.37254121e+00, ...,\n",
+       "          1.37938689e+00,  1.06110374e+00,  4.61172804e-01],\n",
+       "...\n",
+       "        [-3.02078576e-01, -1.88213313e-01,  8.17596888e-01, ...,\n",
+       "         -5.84433439e-01,  4.81188194e-01, -4.71785663e-01],\n",
+       "        [ 7.55166533e-01,  8.01897841e-01,  4.75626737e-01, ...,\n",
+       "          1.33228868e+00, -6.37998166e-02, -1.84531669e-01],\n",
+       "        [-1.23179172e+00, -2.21936013e-01,  2.04777706e-01, ...,\n",
+       "          1.31857556e-01,  6.55100450e-01, -1.83668264e+00]],\n",
+       "\n",
+       "       [[-7.22967767e-01, -1.76971571e+00,  1.42327051e+00, ...,\n",
+       "          2.25718129e-03,  4.04621318e-01, -1.07598447e+00],\n",
+       "        [ 1.94360925e+00, -3.16118802e-01,  1.99686729e+00, ...,\n",
+       "          1.04826266e+00, -1.43272236e-01,  4.90762781e-01],\n",
+       "        [-5.46353580e-01, -6.09092372e-01, -6.50878382e-01, ...,\n",
+       "         -7.10435821e-01,  4.80061194e-02,  1.47524742e+00],\n",
+       "        ...,\n",
+       "        [-1.73257446e-01, -7.84280071e-02,  7.87059969e-01, ...,\n",
+       "         -9.60744471e-01,  2.17777332e+00, -1.34735336e+00],\n",
+       "        [ 4.02375740e-01,  8.00217343e-01,  8.13724984e-01, ...,\n",
+       "          2.11059575e+00,  8.88030645e-01,  1.76982235e-01],\n",
+       "        [-5.75419207e-01, -2.43547991e+00, -2.23312275e+00, ...,\n",
+       "         -6.04373521e-01, -1.11483187e+00, -7.01150792e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xk</span></div><div class='xr-var-dims'>(chain, draw, oid, xk_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1384 -0.1716 ... -0.7012 1.527</div><input id='attrs-bfa75f1a-2241-499e-b3f4-54f561dcfc7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfa75f1a-2241-499e-b3f4-54f561dcfc7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b7fd88d-f8f9-4eed-b0db-5297230d30dd' class='xr-var-data-in' type='checkbox'><label for='data-5b7fd88d-f8f9-4eed-b0db-5297230d30dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 1.38398970e-01, -1.71572202e-01],\n",
+       "         [-1.52722766e-01,  7.43524554e-01],\n",
+       "         [ 1.02847413e+00, -8.27321404e-01],\n",
+       "         ...,\n",
+       "         [ 1.68791563e+00,  2.54917206e-01],\n",
+       "         [-6.76628251e-01,  2.61102907e-01],\n",
+       "         [-1.90084182e+00, -3.23206696e-01]],\n",
+       "\n",
+       "        [[-1.16834250e+00, -1.04426550e+00],\n",
+       "         [-1.64154856e+00, -1.26593499e+00],\n",
+       "         [ 3.34732595e-01,  1.63890212e-01],\n",
+       "         ...,\n",
+       "         [-5.66382826e-01,  1.03644477e-01],\n",
+       "         [ 1.47342455e+00, -7.29909137e-01],\n",
+       "         [ 2.47932387e-01, -4.05472802e-01]],\n",
+       "\n",
+       "        [[-3.02794495e-01, -2.37152057e-01],\n",
+       "         [ 1.71188153e+00,  1.18024281e+00],\n",
+       "         [ 1.53628776e+00,  1.28461333e+00],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [-9.60744471e-01,  5.94168818e-01],\n",
+       "         [-3.24801330e-01,  2.17777332e+00],\n",
+       "         [-1.34735336e+00,  2.46251640e+00]],\n",
+       "\n",
+       "        [[ 4.02375740e-01,  8.00217343e-01],\n",
+       "         [-1.78865226e+00, -2.89581598e-01],\n",
+       "         [-9.38267015e-01, -1.53151709e+00],\n",
+       "         ...,\n",
+       "         [ 2.11059575e+00, -8.97034041e-01],\n",
+       "         [ 1.13768327e-01,  8.88030645e-01],\n",
+       "         [ 1.76982235e-01,  7.10247917e-01]],\n",
+       "\n",
+       "        [[-5.75419207e-01, -2.43547991e+00],\n",
+       "         [ 1.49194568e+00,  6.42474940e-01],\n",
+       "         [ 5.69475854e-01,  2.92061503e-01],\n",
+       "         ...,\n",
+       "         [-6.04373521e-01,  1.51540875e+00],\n",
+       "         [-4.08433291e-01, -1.11483187e+00],\n",
+       "         [-7.01150792e-01,  1.52666248e+00]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9cc2eae-270f-4db7-8721-90a2d941bc4e' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9cc2eae-270f-4db7-8721-90a2d941bc4e' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7886e789-e66f-49bd-8a30-d5fcae952d19' class='xr-index-data-in' type='checkbox'/><label for='index-7886e789-e66f-49bd-8a30-d5fcae952d19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f30aeca3-ee07-4322-be2c-09f149f4849f' class='xr-index-data-in' type='checkbox'/><label for='index-f30aeca3-ee07-4322-be2c-09f149f4849f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>oid</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b683d0f2-675b-4b2c-b1c0-ee98b6df8401' class='xr-index-data-in' type='checkbox'/><label for='index-b683d0f2-675b-4b2c-b1c0-ee98b6df8401' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;], dtype=&#x27;object&#x27;, name=&#x27;oid&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_unobserved_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-621b024e-9ba2-48e7-a544-b74d6c6b3152' class='xr-index-data-in' type='checkbox'/><label for='index-621b024e-9ba2-48e7-a544-b74d6c6b3152' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;xk_unobserved_dim_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xk_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cbd8a70f-05cd-4525-8f6b-5f38a362549b' class='xr-index-data-in' type='checkbox'/><label for='index-cbd8a70f-05cd-4525-8f6b-5f38a362549b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;c&#x27;, &#x27;d&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xk_nm&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54a8c034-e8bc-44a8-86fb-7c64f4cfdf2a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-54a8c034-e8bc-44a8-86fb-7c64f4cfdf2a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-16T10:46:22.072054+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_predictions_constant_data65d898ea-a7d8-450f-8779-87f343a85b23\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_predictions_constant_data65d898ea-a7d8-450f-8779-87f343a85b23\" class = \"xr-section-summary\">predictions_constant_data</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 468B\n",
+       "Dimensions:  (oid: 10, xj_nm: 3)\n",
+       "Coordinates:\n",
+       "  * oid      (oid) &lt;U3 120B &#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; &#x27;o19&#x27; ... &#x27;o26&#x27; &#x27;o29&#x27; &#x27;o35&#x27; &#x27;o39&#x27;\n",
+       "  * xj_nm    (xj_nm) &lt;U9 108B &#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;\n",
+       "Data variables:\n",
+       "    xj       (oid, xj_nm) float64 240B 1.0 0.3722 1.112 ... 1.0 0.7767 -0.3962\n",
+       "Attributes:\n",
+       "    created_at:                 2024-12-16T10:46:22.075888+00:00\n",
+       "    arviz_version:              0.20.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.16.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-31fba6b7-0ff5-4d3d-a714-657d36844eaf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31fba6b7-0ff5-4d3d-a714-657d36844eaf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>oid</span>: 10</li><li><span class='xr-has-index'>xj_nm</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18eb618a-9822-4fa6-af42-d568a70565d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18eb618a-9822-4fa6-af42-d568a70565d6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>oid</span></div><div class='xr-var-dims'>(oid)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;o00&#x27; &#x27;o05&#x27; &#x27;o11&#x27; ... &#x27;o35&#x27; &#x27;o39&#x27;</div><input id='attrs-a81012c1-93ee-4bdd-9512-01cc83d53c69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a81012c1-93ee-4bdd-9512-01cc83d53c69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f738223e-c08e-495b-9bae-653f44015bd6' class='xr-var-data-in' type='checkbox'><label for='data-f738223e-c08e-495b-9bae-653f44015bd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;],\n",
+       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xj_nm</span></div><div class='xr-var-dims'>(xj_nm)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;intercept&#x27; &#x27;a&#x27; &#x27;b&#x27;</div><input id='attrs-21a35213-b508-4238-bc33-cab6992ea075' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21a35213-b508-4238-bc33-cab6992ea075' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77945b07-8f15-4188-8003-fecf355f3fa6' class='xr-var-data-in' type='checkbox'><label for='data-77945b07-8f15-4188-8003-fecf355f3fa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c6ebb6a-d9ad-4c2b-b1b3-8d6c08528f01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c6ebb6a-d9ad-4c2b-b1b3-8d6c08528f01' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xj</span></div><div class='xr-var-dims'>(oid, xj_nm)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.3722 1.112 ... 0.7767 -0.3962</div><input id='attrs-4679fbd6-b9db-4f10-b6d7-519ebf1efdb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4679fbd6-b9db-4f10-b6d7-519ebf1efdb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b81fc40f-a500-4d26-8840-bb1852393666' class='xr-var-data-in' type='checkbox'><label for='data-b81fc40f-a500-4d26-8840-bb1852393666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.        ,  0.37219934,  1.11222061],\n",
+       "       [ 1.        , -1.50719177,  0.36049156],\n",
+       "       [ 1.        ,  0.92549735,  0.95176774],\n",
+       "       [ 1.        , -0.04258335,  1.43469167],\n",
+       "       [ 1.        , -0.20040207, -2.36453796],\n",
+       "       [ 1.        , -0.39603879,  1.18400651],\n",
+       "       [ 1.        ,  0.63838609, -0.45264099],\n",
+       "       [ 1.        ,  0.51968773, -0.22704908],\n",
+       "       [ 1.        ,  1.33623022,  1.03660022],\n",
+       "       [ 1.        ,  0.77672801, -0.39620956]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b7fc9e6-2c90-4996-ab43-ef20f9189d7a' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b7fc9e6-2c90-4996-ab43-ef20f9189d7a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>oid</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c908eeb1-131c-48f1-93f2-b437d1404d4e' class='xr-index-data-in' type='checkbox'/><label for='index-c908eeb1-131c-48f1-93f2-b437d1404d4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;o00&#x27;, &#x27;o05&#x27;, &#x27;o11&#x27;, &#x27;o19&#x27;, &#x27;o20&#x27;, &#x27;o25&#x27;, &#x27;o26&#x27;, &#x27;o29&#x27;, &#x27;o35&#x27;, &#x27;o39&#x27;], dtype=&#x27;object&#x27;, name=&#x27;oid&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xj_nm</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0198d3a3-73d8-48b8-9ab8-d47b82e85ecc' class='xr-index-data-in' type='checkbox'/><label for='index-0198d3a3-73d8-48b8-9ab8-d47b82e85ecc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;intercept&#x27;, &#x27;a&#x27;, &#x27;b&#x27;], dtype=&#x27;object&#x27;, name=&#x27;xj_nm&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b311a40-c001-4efe-af27-878e416b9546' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b311a40-c001-4efe-af27-878e416b9546' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-16T10:46:22.075888+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> predictions\n",
+       "\t> predictions_constant_data"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NOTE overwrite [xk, xk_observed] in group `posterior` and sample yhat into new\n",
+    "# group `predictions`\n",
+    "ida_h.posterior.update({\"xk_unobserved\": ida_h.posterior_predictive.xk_unobserved})\n",
+    "ida_h.posterior.update({\"xk\": ida_h.posterior_predictive.xk})\n",
+    "\n",
+    "with mdla_h:\n",
+    "    ida_h.extend(\n",
+    "        pm.sample_posterior_predictive(trace=ida_h.posterior, var_names=[\"yhat\"], predictions=True),\n",
+    "        join=\"right\",\n",
+    "    )\n",
+    "\n",
+    "# NOTE copy [xk, xk_observed] into group `predictions` to make it clear that these\n",
+    "# dont fit in group `posterior` (the dims / coords issue), and drop groups\n",
+    "# `posterior_predictive` and `posterior`\n",
+    "ida_h.predictions.update({\"xk_unobserved\": ida_h.posterior_predictive.xk_unobserved})\n",
+    "ida_h.predictions.update({\"xk\": ida_h.posterior_predictive.xk})\n",
+    "\n",
+    "del ida_h.posterior_predictive, ida_h.posterior\n",
+    "ida_h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe**\n",
+    "\n",
+    "+ Finally in `ida_h` we have Datasets for `predictions` and `predictions_constant_data` as if we'd done only one step\n",
+    "+ `mdla_h` can be safely removed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5.4 Out-of-sample: Compare auto-imputed values $x_{k}$ `xk_unobserved` to known true values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE**\n",
+    "\n",
+    "+ Recall we can only do this because it's a synthetic dataset where we created those complete (full) values in `dfraw` above\n",
+    "+ We will show a plot that might surprise the reader..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Create index to extract appropriate obs from `xk`, because `xk_unobserved` doesn't have the right dims indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfx_holdout_xk = (\n",
+    "    dfx_holdout.loc[:, [\"c\", \"d\"]]\n",
+    "    .reset_index()\n",
+    "    .melt(id_vars=[\"oid\"], var_name=\"xk_nm\", value_name=\"xk\")\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    ")\n",
+    "idx_h_xk_unobs = dfx_holdout_xk.loc[dfx_holdout_xk[\"xk\"].isnull()].index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Extract `xk_unobserved` from PPC idata , isolate via index, transform back into data domain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_h_xk_unobs = (\n",
+    "    az.extract(ida_h, group=\"predictions\", var_names=[\"xk\"])\n",
+    "    .to_dataframe()\n",
+    "    .drop([\"chain\", \"draw\"], axis=1)\n",
+    "    .reset_index()\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    "    .loc[idx_h_xk_unobs]\n",
+    ")\n",
+    "df_h_xk_unobs = (\n",
+    "    pd.merge(df_h_xk_unobs.reset_index(), df_mns_sdevs.reset_index(), how=\"left\", on=[\"xk_nm\"])\n",
+    "    .set_index([\"oid\", \"xk_nm\"])\n",
+    "    .sort_index()\n",
+    ")\n",
+    "df_h_xk_unobs[\"xk_unobs_ppc_data_domain\"] = (\n",
+    "    df_h_xk_unobs[\"xk\"] * df_h_xk_unobs[\"sdev\"]\n",
+    ") + df_h_xk_unobs[\"mn\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Attach real values (only possible in this synthetic example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_h_xk_unobs = pd.merge(\n",
+    "    df_h_xk_unobs, dfraw_xk.loc[idx_h_xk_unobs], how=\"left\", left_index=True, right_index=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Force dtypes for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_h_xk_unobs = df_h_xk_unobs.reset_index()\n",
+    "df_h_xk_unobs[\"oid\"] = pd.Categorical(df_h_xk_unobs[\"oid\"])\n",
+    "df_h_xk_unobs[\"xk_nm\"] = pd.Categorical(df_h_xk_unobs[\"xk_nm\"])\n",
+    "df_h_xk_unobs.set_index([\"oid\", \"xk_nm\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot posterior `xk_unobserved` vs known true values (only possible in this synthetic example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 355,
+       "width": 1069
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_xkhat_vs_xk(df_h_xk_unobs, mdlnm=\"mdla\", in_samp=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ Excellent: this looks like we have a terrible prediction - but the model is working as expected, and this is helpful\n",
+    "  to illustrate\n",
+    "+ The posterior values of `xk_unobserved` have been pulled from the hierarchical `xk_mu` distributions and are not \n",
+    "  conditional on anything else, so we get pretty much all the same predicted value\n",
+    "+ This should drive home the understanding that while technically this model **can** handle new missing values,\n",
+    "  and **does** auto-impute values for missing data in an out-of-sample dataset (here `dfx_holdout`), these auto-imputed\n",
+    "  values for `xk_unobserved` **can't** be any more informative than the posterior distribution of the hierachical \n",
+    "  prior `xk_mu`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5.5 Out-of-sample: Compare forecasted $\\hat{y}$ `yhat` to known true values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Extract `yhat` from PPC idata, and attach real values (only available because it's a `holdout` set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>chain</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1.118062</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>draw</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>144.340887</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>124.750000</td>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>374.250000</td>\n",
+       "      <td>499.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yhat</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>274.219714</td>\n",
+       "      <td>81.642115</td>\n",
+       "      <td>7.298419</td>\n",
+       "      <td>216.866769</td>\n",
+       "      <td>265.639881</td>\n",
+       "      <td>325.320904</td>\n",
+       "      <td>607.137715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>278.797170</td>\n",
+       "      <td>72.820296</td>\n",
+       "      <td>181.932062</td>\n",
+       "      <td>203.615652</td>\n",
+       "      <td>271.797565</td>\n",
+       "      <td>323.667882</td>\n",
+       "      <td>439.462555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         count        mean         std         min         25%         50%  \\\n",
+       "chain  20000.0    1.500000    1.118062    0.000000    0.750000    1.500000   \n",
+       "draw   20000.0  249.500000  144.340887    0.000000  124.750000  249.500000   \n",
+       "yhat   20000.0  274.219714   81.642115    7.298419  216.866769  265.639881   \n",
+       "y      20000.0  278.797170   72.820296  181.932062  203.615652  271.797565   \n",
+       "\n",
+       "              75%         max  \n",
+       "chain    2.250000    3.000000  \n",
+       "draw   374.250000  499.000000  \n",
+       "yhat   325.320904  607.137715  \n",
+       "y      323.667882  439.462555  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_h_y = (\n",
+    "    az.extract(ida_h, group=\"predictions\", var_names=[\"yhat\"])\n",
+    "    .to_dataframe()\n",
+    "    .drop([\"chain\", \"draw\"], axis=1)\n",
+    "    .reset_index()\n",
+    "    .set_index([\"oid\"])\n",
+    ")\n",
+    "df_h_y = pd.merge(df_h_y, df_holdout[[\"y\"]], how=\"left\", left_index=True, right_index=True)\n",
+    "df_h_y.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot posterior `yhat` vs known true values `y` (only available because it's a `holdout` set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mTeIY5unp6ShdujTat2+PoUOH6i2vmIup93kARh2f69evY9OmTbh//z7CwsJQrFgxtGvXDl999VVO7xKAjPNlzZo1AIA+ffroPb+ePXuGDRs24Nq1a3jz5g3y588PT09PjBgxArVr10aNGjWQmpqqUSZVXn8nTZqEZs2aYdasWbh79y7s7e1Rvnx5LFy4ECVKlBDnz0o5TpU5yoGCIODvv//G2bNn8fbtW+TLl0983tKtWzeTr9VKcrkcR44cwa5du/DkyRMkJiaiXLly6Nmzp9GVB6aWM40tRysUChw+fBgHDx7EvXv3xHy8RIkSaNiwIQYOHKj2PWXWu3dvrF69GhcuXMDdu3d1ntuWpvpcbs+ePShRogTWrVuHkydP4u3bt8ifPz+qVauGkSNHolGjRgCAd+/eYfXq1Th79izev3+PAgUKoH79+hg/fjwqVqyodTuCIODff//Ftm3bEBQUhOjoaJQsWRJdunTByJEjs7UPWSlHU97AyiorFRkZiQsXLgDIeGDz6aefGr2sjY0NBg4ciP/9739IS0vDgQMH9D44sRS5XI5ffvlF41XtlJQUBAUFISgoCFu3bsWMGTPQrVu3HInh8uXL+PrrrzXG4Hr37h3evXuHEydOYMeOHVi9erVGYn39+nWMHz8eUVFRGutVXb5du3ZYunSpzoTnwIEDmDp1qto4He/evYOvry/OnDmD7du34/Lly5g9e7b4FgmQcXP5559/cOHCBezevVvnmyVnzpzBt99+i9TUVPGzp0+f4unTp9i9ezcWLlyINm3aGD5YKs6fP4+JEyciNjZW7fOQkBCEhITAz88Pw4YNw+TJk7OcwBiSlJSEYcOG4dq1a+JnycnJuHLlCq5cuYJdu3Zh9erVcHZ21lhW2Z1m5vjfvn2Lt2/f4vDhw+jYsSPmzJkDBwcHABlvLs6bNw9eXl5ISEjA77//jsaNG4sDMyqXVxaeSpQogVmzZmmNfceOHZgxYwYUCoX4WXBwMIKDg7Fjxw789ddfeh9ua7Nt2zbMnTtXo5/uV69e4dWrV/Dx8cGQIUMwZcoUkx9Cqj6oUkpLS8Pz58/x/Plz7N27FyNGjMDkyZNNWi8APH78GN9++y2ePHmi9nlkZCQuXryIixcvYtOmTVi5cmWWK1aNsWjRIvz1119qnwUEBCAgIADbt2/H33//jXLlypk1/qCgIAwfPhwRERFqn4eFhSEsLAznzp3Dli1bsGHDBpMrMNPT0zF79mxs2bJF7fOUlBQ8ePAADx48wNatW7F48WI0bNhQ53rWrFmDTZs2if8ODQ01aqBhc13fs7p9Y9SqVQsVKlTAkydPcODAAb2VVX5+fgCApk2bav0u5s2bpzGmybt373D48GEcO3YMM2bMQO/evTWW27JlC+bMmQO5XK72eVpaGhISEvD8+XP4+PhgypQpGDZsWBb2Mutat26NgwcPIj4+Hrdv30bdunXVpgcHB4sDE8fFxSEgIECjMHDv3j2xq87MjV2yIyEhAcOGDYO/v7/a5y9evMDmzZuxf/9+bNiwAR4eHiavOyv3hxo1asDd3R3BwcEGc679+/cDAD7//HO1iqrIyEh8++23avc0AIiOjhbva1u3bsWqVat0FpAFQcC3336Ls2fPip89f/5co3IhNTUVI0eOxJUrV8TPkpOTER4ejqtXr8LHxwdLly416Y3V+Ph4/PDDDzhz5oza52lpabh37x7u3buHzZs3Y+HChWjSpInR6zVWUlISJk+ejGPHjql9HhMTg1u3buHWrVvYsmULVq1ahSpVqmgs/+7dO3z55ZcICgpS+1y57Pnz59Xu2abKbs6YkJCAadOmaXS7mpKSgjt37uDOnTvYtWsX1q9fr/cBirmlpqZi2rRp8PX11ZgWHR2N6OhoBAQEYNu2bVizZo3GdSSn5MuXD506dcKOHTtw9epVhIaGam0cAfx3fXdxcUHLli3Nsv0iRYqgadOmOHPmDI4cOYKff/5ZZ6MV5TWhbNmyag/VzVXOyAkBAQH4+uuv8e7dO7XP379/j2PHjuHYsWPo3Lkz5syZozE+TXbKXsbKatknu9cRpRkzZmDnzp3iv0NCQlCkSBGj479+/TomTpyI0NBQtc/DwsJw+vRpnD59GvXr18eSJUvUyiGqFAoFJk6ciMOHD6t9rtyP7du3Y/369RqVyNn5fnL6+OXlc8oc5bSoqCj07NkTwcHBap8r82o/Pz9s3brVpN+aKXLqPi8IAv744w+NPPrVq1dYt24dDh8+jPr165tlH/Q5f/48Xrx4AQDo3Lmzzvm0PbtJTk7G8ePHcfLkSaPKw8+ePcOaNWsQFxcHIONe/v79ezEvzG45zlzlwDt37uCnn35CTEyM2jquXr2Kq1ev4sSJE1ixYoXJz3tiY2PxzTffqOWhAMTYTp48qXfssZx8jiiXyzF+/HiN33lMTAxiYmLw4MEDbNq0CX/++SfatWundR0lSpRAzZo1cevWLWzbti1PVJY8evQIY8eOFct2QMbxvHDhAi5duoQ//vgDn376KcaOHav2e4iIiMCRI0dw9uxZbN++XePanpqaiqlTp+LgwYNqnz958gRLlizB0aNHTXrWrcqay9GUfRyzykr5+/uLAyJra81mSNu2bcVE59KlS2aJqUSJErh586bY4gTIeJB48+ZNrF271uT1TZ06VbzB1KtXD+vWrcPly5fx77//4vfff0exYsWQnJyMyZMn4+jRo+Jyhw4dws2bN8W3Wj799FPcvHnT5DiSkpIwceJEJCYmomTJkli0aBFOnjyJy5cvY/fu3ejYsSOAjBYXmfswDQ8Px9ixYxEVFQUXFxdMnz4dR48exZUrV3Do0CH89ttvYvJ//PhxtfhVpaSkYOrUqXBzc8P8+fNx7tw5HD58WBzMMyYmBl9//TVmzZqFzz77DBs3bsSVK1dw4MAB8eb45MkTbN++Xed+Tpw4EVKpFJMmTcLp06dx/vx5zJo1Cy4uLkhOTsa3335r0oCLt2/fxtixYxEbG4uSJUtizpw5+Pfff8UHacrC38aNG/Hnn39qLG9ra4ty5cqhXLly2Wr5fuPGDVy7dg1NmjTBtm3bcOXKFfj4+IjJ5Y0bN7S+8nz+/Hl8//33iI2NRaFChTBt2jTxe9+8eTOaNWsGADhy5AgmTJigNjB5qVKlxG41o6KiMHPmTHGaQqHAjz/+iNjYWNjY2GDhwoVaK8rCw8Px22+/oUCBApgxYwbOnz+PM2fO4Mcff4SDgwOio6MxYsQIjQKqPrt378aMGTOQkpKCEiVKYN68eTh79iwuX76MNWvWiK3XNm3ahNmzZ4vLde3aFTdv3lRrsaY8l5Sf3b17V/weGzRogI0bN+LcuXO4cOEC1q5dKz4MXr9+PS5evGh0zEBGwXD48OF48uQJbGxsMHLkSOzfvx/+/v7w8/PD0KFDIZVKERwcjKFDh4qFy7p164pxKn311VfiZ6a2gL9//z7++usvFC9eHH/++ScuXbqE48ePY8yYMZDJZHj9+jWGDRumteCelfiVJk2ahIiICLi4uGDWrFk4fvw4rly5Aj8/PwwcOBBARoXAnDlzxGV+++033Lx5Uyx8e3p6ivut+hBQtYDSpk0bbNmyBVeuXMG///6LuXPnokSJEoiOjsaYMWMQGBio89hs2rQJ9erVw759+3D+/HnMnz8fffr0MXhMs3p9N9f2jaUswDx79gx3797VOo+yhTaQ8ZZPZqmpqVi/fj0qVaqExYsX49y5czh69CgmTZqEfPnyIT09HbNmzcL79+/Vljt//jxmzpwJuVyOypUrY/ny5Th16hQuX76Mffv2YcyYMbC3t4cgCFiwYIHaNeHmzZtqD61nzJih8Vl2NWvWTHyLR9l4RlXm3CJzgRMA/v33XwAZD251vW0L/HdOz5gxQ/xMea/X9rbHrVu34O/vjxYtWmDz5s24dOkSfH19xdarMTExYh/kpsjO/UH5ZsbDhw/x6NEjreu/d+8enj59CkD9t5SamorRo0fj2rVr4rXEz88PV69eFbfl6OiIoKAgjBgxQud4pM+fP8fZs2fRr18/nDp1CidPnsT06dM18kjl9cDT0xObN2/GlStXsGfPHvGNkgsXLuDHH380+rilp6dj7NixYsG+TZs22Lp1Ky5fvoyTJ09i6tSpKFiwoHjNUb12r127Fjdv3hQLql988YV4TStRooTB6UDGg66JEyeKD0h79eqF3bt3w9/fHydPnsS0adNQqFAhvHnzBsOHD8fbt2/V4pfL5Rg9ejSCgoIgk8nw5Zdf4ujRo7h8+TLWrVsHDw8P7N+/X2M5Y5kjZ5w6dap4frdu3Vo8vocPH8aXX34JqVSK58+fY/z48VAoFEbd381h6dKlYkVV27ZtsWXLFpw7dw7nzp3D1q1b0aVLFwAZlW3KcUByi/J6oFAocODAAa3zyOVy8bh27txZ55smWaG8JkRFRWm9hgIZvw3ltVT1gZq5yhk54dWrVxg2bBjevXundp309/fH7t27xYYZhw4d0ni7LztlL1NkpeyT3euIUmpqKnbu3Il27drh8OHDOHv2LGbMmIFOnToZFXtgYCC+/PJLhIaGwtHRERMmTMCRI0fE4+vl5SUeo9GjR+t8oPvixQscPnwY1atXF3vvOHToEAYNGgQgo/w4ZswYtQfu2fl+cuP4WeqcMuY+pE9Wy2mZLVy4EI8fP8aQIUOwf/9+XLlyBTt27MDnn38OIOM7X7x4scF4siI793lD1q5dK1ZU1a1bV8xLDh48iMGDB+Pdu3dio4KcpLxPuLu76/xer1y5Am9vb6SlpYnnyKVLl3D69GlMmTIF9vb2mDNnjlpFuTY+Pj4QBEFcfseOHZg2bZpY6ZPdcpy5yoHe3t4QBAE//fQTTp48iUuXLmHFihXi8Tl9+rTO+6s+EydOFMsNffr0EX/PW7duRePGjXH58mW9XfBmtZxpTDl648aN4u+8X79+2Ldvn/g7nzNnDgoXLoy0tDRMnTpV71iWyreLjx8/bvD3YA1+/fVXxMTEYNKkSThx4gT+/fdfTJ8+Hba2tlAoFJg9eza+/vpr2Nvb448//sCFCxdw5swZjBs3DhKJBImJiVqf/c2aNUusqGrbtq14X9i7dy+6dOmCoKAgjYpBY2SnHE15hEBWadmyZYK7u7vg7u4uXLt2LUvraNKkieDu7i40bdpU7fOWLVsK7u7uwogRI3Que/DgQXH7V65cUZt25coVndOMdfHiRXEd33zzjSCXyzXmCQ0NFZo3by64u7sL9evXF+Li4tSmT548WXB3dxdatmyZpRjOnDkjxnD16lWN6QqFQhgwYIDg7u4utGjRQm3aokWLxGUvX76sdf0BAQHiPFOmTFGbtnfvXnFarVq1hDdv3mhsu2PHjuI83bp1E1JSUtTmSUlJERo3biy4u7sLQ4cO1bn+qlWrCpcuXdKILzg4WKhZs6bg7u4u9O/fX+fyt27d0hpXu3bthKioKK37/ssvvwju7u5C5cqVhUePHmmdJ6uUv1/lbyc9PV1jnmnTponznD9/Xvw8JSVFaNWqlfibevnypcayCoVC+N///icu7+fnpzHPN998I04/efKkIAiCsHr1avGzv/76S2OZpUuXqn3nwcHBGvNcunRJqFKliuDu7i788MMPOpd///69+HlkZKTg6ekpficREREa601NTRVGjx4tLn/jxg2d685s7ty5gru7u1CzZk0hKSlJY3pUVJRQu3Ztwd3dXfjxxx81puszadIk8Xdy4sQJrfP4+fmJsf38888a05XTli5datK2BeG/a4jyOvnu3TuNeVTPhUWLFpkt/uDgYPHzffv2aV1Wuf7q1atrHPtBgwYJ7u7uwqBBgzSWu3XrlrjuuXPnal13RESEeH3t3bu32jTVa3zdunU1rr1KyuNXvXp1tc+ze303dvvmEBoaKlStWlVwd3cXZs6cqXUe5TlQr149teuw6u/niy++EOLj4zWW3bBhgzjPrl271Kb1799f3H9t523m5TP/Tl69eiVO27t3r4l7bpxhw4YJ7u7uQq9evTSmjRo1SoxfV07RvXt3wd3dXZg2bZr4mer3e/DgQbX5Vc+3V69eaaxP9fr/008/aY15zJgx4jwvXrwwel+ze3+IiIgQPDw8BHd3d2HBggVatzFz5kzB3d1dqFOnjto5rbx/VK5cWTh9+rTWZW/evCn+VufPn682TfUaPmTIEJ37qHr8RowYIaSmpmrMM2XKFHGezLmDrvxx165d4jKzZ8/Wuu3Hjx8LderUEdzd3YX27dtr3LuV6548ebLe2LVNV81Zt2zZonX5Fy9eiPeq77//Xm3apk2bxOV37NihsWxSUpLQs2dPcR5dMeqS3Zzx7Nmz4rRff/1V6/J//fWXOI/q/Ujf/V0Q/ruHqp6jmanmN6rnZXx8vPDZZ58J7u7uwuDBg7XmY4Kgfk6GhISoTTOmTJIdnTp1Eq/R2qiWBe7cuWPWbaempgqff/654O7uLkyYMEHrPMprfJUqVdTKAuYsZ6jm8YKQ/XLgiBEjBHd3d+Hzzz/Xep0WBEFYs2aNuPzZs2fFz7NT9jIku2Wf7F5HVLffrl07rbmPMZS5QfXq1XX+JleuXClua+XKlWrTlPmhu7u70LdvXyE5OVljedXrheo1LzvfT24cP0ueU4Kg/z6k65wzRzlN+bmuY5uSkiLG5unpKSgUCq37lx3Zvc/rOj7v378Xz8lBgwZpzUvWrVundgxygkKhEPNZXffD9PR08Z7SokULITw8XGOeW7duibmgtvxcNQ/TVf7LbjnOnOVADw8P4cGDBxrLP378WKhcubLg7u4ufPXVV1q3ocupU6fE9S9cuFBjulwuF8aOHSvOk7msa47niPrK0T169BDzGm1Ut79nzx6d+3np0iVxvgsXLuicz5JUy5KZ80el6dOni9Nr1aqltXykfDZWvXp1IS0tTfw8MDBQfLY1ceJErdem3377TVx/5ue6qvGtXr1abVp2y9Fk/fhmlZWKjo4W/y5QoECW1qF8BVzbq+6WpnwTyN7eHjNmzND6unvRokXFFnnR0dFZarWhj2oLB2U3RaokEgl++eUXbNy4Ua0bKgCoUKEC+vfvj759+4qtmTKrVq2a+N3p+w569Oih8YaRRCJRe0ti+PDhGq097ezsUL16dQDQ6IZDVc+ePbW+3l2pUiUMHToUQMZbSK9fv9a5DqVLly6J3Z1NmDBBZ3dcP/zwAxwcHCAIAnbs2GFwvVlha2uLX375Retr5z/++KPYNcXevXvFz5V9hAPAd999p7VbOYlEgv/9739itxqZX58HMt5iUJ5fv/32G27cuIGlS5cCABo2bIjRo0frjf2rr75CpUqVND5v2LCh2PLy+PHjSE5O1rseADh48KDYhcBPP/2ktTsQW1tbzJo1S3xLwpRxwpQtLtPT09WuS0ouLi5YtWoVtm/fblJL/NjYWLFrkg4dOujsirJr165itzy+vr463yjILm9vb61dBPXo0UPsklHZrQiQ/fgNXX+AjN/ounXrsH//fpNaeyu/XxcXF3H8tMwKFy4sdnt3584dnW9XNmvWzOT+7815fc/K9k1RtGhRcfyhw4cPq3W1CmS0yFe2BtPX6v7bb7/V2mVahw4dxL+VXYsAGS2QmzZtih49emD8+PE6u/FRvb9Y4l7eqlUrABDHT1JKTU3F9evXAfw3duWNGzfUftfv37/HgwcPAJi3C0Ago1tWXYMFqx7z58+fG73O7N4fChcujObNmwPIuC4LKm9dARlvcCivGZ06dRLHFlJdT/PmzXV2Q1a7dm3xzeGdO3dq/FaVjGm9L5PJMHPmTK3jX02dOlXs3tDYlszKc/6TTz7R+b1UqFBBvOY8e/ZMZ6v4rFBe8ypVqiS+lZpZ6dKlxd/q0aNH1VrDKq8/lSpVEsexU2Vvb5+lN/WUspszKr8HZ2dnnffZwYMHo2LFimjUqJHernPMKT4+HkOHDkWXLl3w9ddf6+wGyJLXMeWbFUFBQRpdZwEQ3wqrWLGi2bvpsbW1FXuBOH36NBISEjTmUX63DRs2VCsLmLOcYU7Pnz8Xz91Ro0ZpHb8MAEaMGCG2vN+2bZv4eXbKXqbIStknu9cRVe3bt8/SuH9BQUHi2LhDhgzR+ZscM2YM3N3dAQBbt27Vub5ffvlFoxtGABg9erT4/aiWk7Lz/eTG8cuL55Q5y2lubm5i7yuq7OzsxLc44uLicuR6kFP3+ZMnTyIpKQlARllMW14yfPhwrV2xm9OzZ8/EPDfzuLBKt2/fxuPHjwEA33//PVxdXTXmqVWrltbvKDMbGxud4yNmtxxnznJgq1atULVqVY3PK1SoIH4nxjxDUqXMuVxcXDB+/HiN6TKZDNOnT9eZU+T0c0TldTAmJkbtzVOlhg0bYsWKFfDx8dE7xqXq7+jWrVtGb99S3N3dtT7PUO2Cs3PnzlrLR8pnJampqWrXn4MHD0KhUEAmk2Hy5Mlau1b94YcfTH7enVfK0ZQ9rKyyUqoPqbPaJYWufpytgXKciYYNG6JQoUI652vdurX4QCfz2BTZVatWLTEh+vHHH/HLL7/g7Nmzal19ubu7o2HDhhoX5S+++AK//vqrzsGo4+PjceHCBfEmm7kfVVW6xiZSTYCUlVKZKbuZ0/dqsbILFm1UHyAa012kajdPVapUQUJCgtb/JBKJ2F+t6oC05lSvXj2tSSIAODk5oV69ehrbV41f2ZWFNvny5RNv1vfv39coDBUqVAhz5syBRCLBu3fvMHToUKSlpaFw4cL4448/DPbbrO87UW43OTlZLLDqo9wnZ2dnsYsqbYoWLSomG6acS8rjmJqail69emHp0qW4deuW2oPSBg0aoE6dOjq/D22uXr0qrsPQw1XlA9q0tDSjjompbG1t9Sabyu8kJCREfPid3fgrVqwoXvsWLlyIH374AceOHVMbJ6dUqVJo0qQJypUrZ1Jf4Mrvt3LlymKfzdr+U30IknmcHKVq1aoZvd3M2zfH9T0r2zeVsquoiIgIja4sL126JHbfp60LQKU6depo/Vx13ADV64hEIsHYsWMxZ84cDB48WOuy79+/V+tGRVuBKacpK6sUCoXaPeLmzZtITExEkSJF0KtXLwAZ3QepdqV49uxZCIKA/Pnz6+0PPysqVKig87dVtGhR8W9tD7J0Mcf9QflbevPmjca16uLFi+L4dKq/padPn4pdU3h4eOg8XxMSEsSugmJjYzXGVlIy5pzx9PTU2T+8cqBkZcyGxMbGipWSbdu21fqgSUl1DAhz5XSJiYm4d+8egIyxw4w5fgqFQvx+4uPjxeX13UNr165t8tiBStnNGZXfQ6NGjcSKxMwcHR1x6NAhbNiwQe9YG+ZUrFgxTJw4EQsXLtT68FcQBDx9+lR8uAfoz4dzgpeXl/ggS7XBCZBx3E+fPg1A//U9O5TXhKSkJJw4cUJt2uPHj8VzJ/P2zVnOMCfV62TVqlV1nmvJycni+aZs2ABkr+xlClPLPtm9jmSW1dxF9fjqyy0lEol4noeFhWmMmwoAZcqU0fqQGcho8KFsXKF6H8vq95Obxy+vnVPmLKfVqFFDZyWoar5pTGNHU+Tkff7y5csAMipPdFXOSiQSk8fXNpXqOVShQgWt8yjHA5VIJHobYRnTaKhcuXI67+fZLceZsxyobwxtZU6krGw0lvK6+/nnn+t8zvnJJ5+gRo0aWqfl9HNE5Xn48OFD9O7dG5s2bRK78Ab++z16eHjobVBZuHBh8dmItmu0tdH1XatWBOl6Jql6HFQbTCm/62rVqqmVz1Q5ODiYPJZtXilHU/ZYb23GR061djnzIN/GUrYOMddg9MZITk7W2doXAPLnz4/4+HhxUL6KFSvqXZ+NjQ3Kli2Lhw8f4s2bN0bFkJ6erjdJs7W1hZ2dHYoWLYrJkyfj999/R2pqKnbs2IEdO3bA1tYWtWvXRuPGjdG6dWutb8AoKRQKXL16FQ8ePMCLFy/w+vVrPH/+HCEhIWqtqjO3sFal6wG/6sNpXTdCY1rt6WodBADly5cX/zZmLAbVNwP0PdxXldUxHgzRlUgqlS1bFmfPnkV4eDhSUlKQL18+MRY3Nze9yY3q+tPT0/Hu3TuN7TVt2hSDBg3C5s2bkZaWBolEgrlz5+q8ESvlz59fZ0tUAGotx4w5dsp5ypcvb3Ag4AoVKogPTJXHxJC2bduiY8eOOHLkCMLCwrBixQqsWLFCfKDZtGlTtG7d2uTBfFX3zdB3qXqdMPY6YIpy5crpbRSQ+TspW7ZstuPPly8fZs6ciQkTJiAtLQ0HDhzAgQMHIJPJ4OHhgcaNG6NVq1Ymt/ROSEgQB0b19/fXWYmSma7jaug8yczc13dTt58VrVu3houLC6Kjo7F//361hwnKFroVKlTQ+V1IpVKdcapeo3XdG5OSknDhwgU8evRIHGj76dOnYsWGJZUoUQKVK1dGUFAQLl68KBbAlYWPhg0bolixYihTpgxevHiBK1euiG8FK/sfb9asmVnHgQH0/y5U750KhcLodZrj/tC8eXO4ubkhPDwc+/fvV3tDWvlbKlu2rNp5qXpfVV5fjfHmzRutD/SMOWf05QVAxjXv7NmzCAsLg1wu19v46e3bt2KOY+haWKxYMTg7OyMuLs5s1/LXr1+LDxV9fHzg4+Nj1HLK7YeGhoq/kzJlyuhdpnz58jrfhDVGVnLGlJQUsTVo2bJls7ztnBYaGorLly/j2bNnePnypXgdy1xhrC8fzglFixZFkyZNcPbsWRw8eBCTJk0Sc6UjR44gJSUFMpnM5LEujVWlShV4eHggICAABw4cUBtDR/WNubZt22pd3hzlDHNSvV4NHz7cqGXi4uIQFxcHZ2dns5S9jGFq2Se715HMspq7qOaWhnIo1ekhISEa11/V/dRGeT1RvY9l9fvJzeOX184pc5bTdL09AKg3bjYl9zFGTt7nlcfH0P3X0HazS/Xc0/XsTPkGUZEiRfRWUhg6dwHdv/HsluNysxyo/M2Zcq4kJyeLzyiN+c4zj1uV088RAWDcuHG4dOkSnj59isDAQPz+++8AgOLFi6NRo0Zo1qwZmjVrJvbio0/BggURERGhtxckVcY+x8wJuq4v2XkmqdzvnDy/rbkcTdnDyiorpZoEPnr0yOQHlvHx8eINNadv7qpGjx6t902aoKAgtYKrtm6TMlPeCFRbdelz/fp1sYsBbcaPH49vvvkGQEa3KTVq1MCGDRtw9uxZJCUlIS0tDVevXsXVq1exaNEi1K5dG7/99pvY3YLS/v37sWLFCq3dDBUrVgyNGjXC6dOnxRuqLrpa1agylNzqo3z7ShvVboiUXRToY8w85ljGGIYSBNXjmpSUhHz58oldsBnzu1OdR9dv7/PPPxdftZfJZEa9WaTv+8gctzFd3pmyT6rHLCEhwajKKqlUisWLF6NNmzbYsWMHbty4AYVCgdjYWJw8eRInT57EjBkz0LlzZ0ybNg0FCxY0uE7VuAHdiY+2uI29DpgiK9+JOeJv27Yt/Pz88Pfff+PkyZOIjY1Feno67t69i7t372LVqlWoVKkSfv31V7WH3vpktZtEXeepMb8RVea+vpu6/ayws7ND586dsXXrVpw6dQqJiYlwdHREQkICTp48CUB/q3sbG5ssXaPT0tKwcuVKbNu2TaOLTYlEInZLZexDn5zSunVrsbJKSfm38m2Kzz//HC9evIC/vz/Gjx+P1NRUsbVsTrSGzYmCmjnuDzY2NujatSvWr1+PY8eO4X//+x/s7OwQHx+PU6dOAfivVbhSVu+Rus51Y84ZQ9csZW4gCAISEhL0XtdV4zD2nI+LizPbtTyrx0+5nGqOZigfM3Sv0CerOaPqtcGYfDG3hYeHY968eThy5IhGq1XlA+6CBQvi33//tUyAyDjnzp49i7dv3+Lq1ato0KABgP8ebDdu3NhgQ6Psbj8gIACXL19GeHg43NzcIAiC2C1R5m5BlcxVzjCn7JxvyvMnO2UvY5la9snudSSzrOYuyuupjY2NwXUYyo0NXY9Vl1d9OJqV7ye3j19eOqfMWU6zVK85OXmfVzbKzsn7rzFUyy+6tqVsOGIoVmMqMXTlsdktx5m7HKjvLbqsMCXn0par5vRzRCCj0mbfvn3YtGkT/Pz8xLfD3759i71792Lv3r1wdHTEqFGj8PXXX+stAyp/S8Z+L6Y8xzQ3Y363ppZ3jT2/s9Llf14pR1PWsbLKSjVq1AgSiQSCIODChQvo2bOnSctfunRJbMXdqFEjk7dv6uu8psiciBmivLjnVCG9Vq1aWLJkiTgGh7+/P65cuYK7d+9CoVDg1q1bGDJkCA4fPiy2ONi5cyemT58OIOMtuDZt2qB69eooX748KlasKL5l0qxZs1wtRGqTnJys82aueuPW11pLSZn4Fy5cWHwQaSmGfqPKfZNIJGKioPztmfK7U11OVWRkJH799Vfx33K5HN7e3ti3b5/WApKSoa4ZVL8TY1pmmrJPqvMYk5Co6tKlC7p06YLo6GhcuXIF/v7+uHTpEp4/fw6FQoEDBw4gNDTU6PGwVLcfHx+vdbwo1elKOXEdyMp3Yq74K1SogDlz5mDmzJm4c+cOrly5gitXruDWrVtIS0vDo0ePMHLkSOzdu9eolnqqv72RI0eaNI6YOVjb9d1Y3bt3x9atW5GYmIiTJ0+ia9euOHHiBBITE3Os1f2UKVPE8bBKliyJ1q1bo0qVKuJ9xMnJCS9evLB4kt2qVSusXLkSb9++xZMnT+Dq6ip2B6Ps3q9Ro0bYuXMnbt++jeTkZFy7dg2JiYmwtbUVuxqydua6P/To0QPr169HdHQ0zp07hzZt2uDYsWNITk6GVCqFl5eX2vyqv/01a9bkyvEyNKaR8ppnY2NjsB951fzCEue86nqmT5+uc6wUXVRbUBt6mJHVsaCykzOq7p+5u3Yylq58KyEhAQMHDhQf/NasWRNNmjRBpUqVUK5cOZQvXx52dnbYvXu3RSurWrVqJb49e+DAATRo0AAhISFi93Q51QWgUpcuXTBv3jykpqbi0KFDGDp0KPz9/cWW/Nq2b+lyhq7vXDXHuHXrlsm5pFJWyl6mMLXsk93riLkoj6dcLjfYA4Kh3NhQOUn1ep35Om/q95Pbxy8vnVO5VU7LSTl5n1feg3Pq/mss1Yfwut4SUV7/DMWanedo2S3HWbocaIjqs42sfOe5Vc60t7fHl19+iS+//BKvXr3C5cuX4e/vj8uXLyMiIgKJiYlYunQp0tPT8e233+pcj/KtJFO68/+QuLi4ICwszOB3rW9IE13ySjmaso6VVVaqWLFiaNy4MS5cuIDjx4/j1atXJvXdvX79egAZDxoy99utvFjq67czqwPQGfOg2tnZGQULFkRMTIxaP/bapKWliV1OKAeCNaRBgwY6x3LQx87ODo0aNRIr98LDwzFnzhwcPHgQUVFR8PPzw/Dhw5GSkoKFCxcCyHgdePfu3Vq7QFMoFBq1/Jbw8uVLnX2Wq/a/a8zxVc4TGRmJ9+/f622JKghCtt4IMyQkJETvdGXfwCVLlhSTTmX3e+Hh4YiKitJbGfTo0SMAGcmr6gC9StOnT0dYWBhsbGzw/fffY8GCBXj69Cn++OMPsTCkTXR0NGJiYnS2VDf1OylZsiQCAwPx9OlTKBQKvcmQcp9cXV31Vqjp4+Ligg4dOqBDhw4AgODgYHh7e+Phw4e4evUqAgIC4OHhYVTcSk+ePNH7BqgybsD464ApXr16pff3qvqdKOM2d/w2Njbw9PSEp6cnxo0bh7i4OKxYsQIbNmxAcnIyduzYgf/9738G96VAgQJi9xtZuQ5mV05f33NKjRo14O7ujuDgYJw4cQJdu3bF8ePHAWRUxOirjMyKO3fuiAl28+bNsWLFCq2tF3UNPp6bqlevjmLFiiE0NBQXL15EsWLFoFAoUKZMGXHco88//xxSqRSpqam4ceOG+GC6fv36Od4a1lzMdX+oVKkSatSogXv37uHIkSNo06aN2Nq7UaNG+OSTT9TmVx076uHDh3orq8x1X1Xtyksb5TXv008/Nbg95TyCIBjsk//t27fiwwVznfPFixcXt2/omqft+BUrVgxSqRQKhULtWq/Nq1evTI4vuzmjs7MznJycEB8fr/VtAFXbtm1DbGwsKleujJYtWxoVn3Lfs1Im2Lp1qxjThAkTMGbMGK3zWfo6Zmdnhy5dumDLli04deoUfvvtN5w4cQKCIKBAgQJ6xx4xBxcXF7Rq1QpHjx7FkSNHMHToUPGaUK5cOY1xInK6nJGdcqDqeRsUFKR3PBNjrlfGlr1MZWrZJ7vXEXNRzS0fP36sN59WzS21dS9ubDkpX758OnMcY7+f3D5+1nZO6ZPb5bSckJP3+eLFi+P27dt49uyZ3t/Gy5cvTQ/cBKpvduh6sK7syiw8PBzx8fE63wZ59uxZluPIbjnO0uVAQ+zs7ODq6oqIiIgs5VyWKGeWKlUKpUqVQp8+faBQKHDu3Dl4e3sjNjYW//zzj97KKuVvyZi3wICsP8e0VsWLF0dYWJjB79rU8zsvlaMp6z7OKt484rvvvoNUKoVcLscPP/xgdI3zunXrcOvWLQBAz549NQbRVrYs0FchpToYXWbmSNDr1asHIGNQTX1xnDp1SmxVYWyfu8Zau3YtunbtitatW2vta9fNzQ3Tpk0T/63sc/XRo0dii6uOHTvqHKvn+vXrYuy53Ue/KuW4IdooB6aVyWRo2rSpwXUpB5wE/us+RZukpCQ0bdoUzZs3x+TJk02I1ng3btzQ2coqIiJC7I5SNWbl7w7IGKtAl5SUFHHQ7apVq2q0btuzZ4947L788kuMHj1a7Npp69at4gCsuuhrYaxcr4uLizgIsT7KfYqLi8P58+d1zvf+/XtxIOPM55K+c3rSpElo164dJk2apHW6u7s7xo0bJ/7b2D6Z69SpI1YiHj58WO+8yu9KJpMZdUxMFRsbq3OQZ+C/76RKlSrig+bsxu/r64sePXqgQYMGWrtecHZ2xuTJk8WCkLHHVSKRiF0G+vv7613uyJEjqFWrFjp06ICjR48atX5jWMP1PSuULXEvXLiA6OhocVymzN22mYPq761///46u9lQ7Xov8xgEOdkYIPN2WrRoIcajvLYquwAEMq5XygeDly9fFu87pj4Ezq190sZc9wfgv9/Mv//+izdv3ojHTFtr7ypVqoiNFw4cOKA3X5g5cybq168PLy+vbD248ff31/kwJiYmRhxoW/m961OgQAFUqVIFAHD8+HG9D8BVr5XmOucLFSokdp19/PhxvS2a161bhzp16qBLly7iOZg/f37xmnny5EmdY8s9e/bMYCWfNtnNGSUSCTw9PQFknFu6ygKCIGDZsmVYtGiRWitSQ+dUdsoEqp/rGuAa0H8dyy3Kcy8yMhI3b94Uu+Xs1KlTrnQ3q7wm3L59GyEhIWJjCG3XhJwuZ2TnOze2HCAIAry8vNCkSROMGDFCjC+rZS9TmVr2ye51xFxUj6++3FIQBDFvc3FxURtbVSk4OBjv37/Xunxqaqp4H6tdu7bYJVlWvx9LHD9rOqf0MUc5zdJy8j6vbKATExODK1eu6JzPUNk6u1QbHoWGhmqdR9mbgCAIesvy+q4/hmS3HGcN5UBDlN/5pUuXdHaPFxcXJz7PzCwny5nv3r3D0KFD0bRpU2zfvl1julQqRYsWLcReEuLj4/V28af8LWV+HvuxUH7XDx8+1JlDp6en48KFCyat1xzlaLJ+rKyyYp999hm++uorABmJ2MiRI/UOFKdQKPDXX39h/vz5AIDSpUvjhx9+0JhPOaDqo0ePEBwcrDFdtVW0Nqr9JetLVvTp378/gIxuGn755RetDwfCw8Pxxx9/AMh45TfzG2LZlS9fPgQFBeH169fYv3+/1nmUXR0B/7WmUd1/XS06IiMjMWPGDPHfWT1O5rBhwwatLVMePnyILVu2AMh4oGhMNxstW7YUk7lVq1bpbPmxePFihIWF4d27d0Z1XZYV0dHRWLx4scbnCoUCM2bMEI/5gAEDxGktWrQQk4UlS5bobCU9e/ZsMfnp06eP2rRXr15h9uzZADIqasaOHQsg41VkZeHn559/1tuSY8mSJVpb7l24cEFMFrt3725U3+ReXl7iw9LZs2dr3a5cLsf06dPFY5J5n1S3k/lBWEpKCl68eIHTp0/rbBUTEBAg/m3sAPCFChVCx44dAQBHjx4VxwbK7NChQ+L1qH379kaPiWWqefPmaa383L17N+7evQsA6NWrl/h5duN3dnZGQEAAoqOjdb6R+uLFC7F1YuaBSZVJmbZri/I3n5aWhp9//lnrw82oqCgsXrwYSUlJCAkJQc2aNbXGkBXWcH3Piq5du8LGxgaJiYlYuHAhkpKScqzVvWoXI7ruI/fu3cPff/8t/jvzd22Oe7GxlMfg6tWrYuKvLLQrKf/t4+ODkJAQSCQSk8erys19yswc9welLl26iOMkzpo1C+np6ToHfJfJZOJ6Hj16hBUrVmhd5927d7F7927ExMRALpeb9LZ9ZvHx8fjzzz81PhcEAbNmzUJKSgqkUqnRFbXKcz40NBQLFizQOs/Tp0+xatUqABkPhTI3kNF3TTM0XXnNi4qKwsyZM7U+ZHz16hX+/vtvJCQkICIiQu2ti969ewPIeBNBGaOq9PR0cYBtU5kjZ1T+PmJiYrTmPQDwzz//iPf/L774Quv2td0LlPfsK1euaM0fDh48qDPXU72O6Wptv3XrVvj7+4v/tlQ+XL16dXF8nb1794qVMTndBaBSkyZNULRoUQiCgN9//x2xsbGQSqXo1q2bxrw5Xc7ITjnQw8NDzBd2796ts0vwzZs3IygoCGFhYShdurRYaZrVspepslL2ye51xByqVKkivhW0adMmMf/MbO3ateJ317t3b61v6ygUCsyaNUvrfixcuBDh4eEA1MtJ2fl+cvv4WeKcMnSf0sYc5TRrYI77vDZt2rQRuwKcPXu21gf/Bw4c0FlxYS6qPWToeqjepEkTMfdasmSJ1u4inz59in/++SdbsWS3HGfpcqAhyuFNEhMTdeZWf/zxh85Kb3OUM3Wdy0WKFBEr+rdt26Y1BkEQEBgYCCDjLUhdb9jFxMSIz3v09cDyIfPy8oKdnR0EQVB7PqdqzZo1YheuxjJHOZqsHyurrNx3330ntla8evUq2rZti3nz5uHatWsIDQ1FVFQUgoODsXXrVnh5eWHRokUQBAGlS5fG33//rXWsgXbt2gHIuNCOGTMGJ0+eREREBJ49e4ZVq1ZhxIgRervtUe3f/9ChQ3j//r3Jr1g2adJEvGkcO3YMQ4cOxYULFxAZGYnQ0FD4+PigV69eYhcGM2fOzFKf5fr06NFDrFyYNm0aFi9ejIcPHyIqKgrPnz/Hjh07xLdJChUqJI5ZUqlSJfHtinPnzmHGjBkIDg5GVFQUnjx5go0bN8LLy0vtwmlMn7o5JTY2Fv3798e+ffsQFhaG0NBQbN26FUOGDEFycjKcnJzw888/G7UuW1tb/Pbbb5BKpUhISMCAAQOwdu1aPH/+HJGRkbhz5w4mTZqEjRs3Asg4Vplb24aGhordyGWnH2WZTIb169dj8uTJePDgAaKionDz5k2MHj0ax44dAwAMGjQI1atXF5exsbER44+OjkafPn2wdetWvH79GlFRUbh+/TrGjBmDHTt2AACaNm2Kfv36icunp6fD29sbCQkJkMlkmDNnjtgasWDBguIYVmFhYXq7AgwJCUHfvn1x4sQJREZG4tWrV1i9ejW+/vprCIKAEiVKGD14pouLC6ZOnQoAeP78OXr16gVfX1+EhoYiMjIS586dw+DBg8VWXn379kWzZs001qG0Z88eREZGim/7jB49GlKpFImJiRg+fDh27dqFZ8+eISoqCkFBQVi4cCHWrl0LIOO8NiUZ+/HHH+Hq6gpBEPDdd99h/vz5CA4ORkxMDIKCgjB37lx4e3sDyOiqSbU1p7ndvXsXAwcOxKVLlxAVFaXRpWONGjXUCvTZjb9ly5aoXLkyAGDZsmWYOXMm7t+/L/4eDhw4gJEjR0IQBNjZ2an9DoH/vrOgoCBcv34dkZGRYmVbs2bNxOvrhQsX0K9fP/E6//btWxw+fBgDBgwQu28aM2aM1q4us8oaru9Z4ebmJhasd+/eDSCjBW5OtLpv0qSJ+HBp+fLl2LRpE16+fInIyEjcvXsX8+bNw4ABA9QKSZnfhHFxcREfAB4/fhxv374VHz6ZW8OGDeHo6IjExEQ8e/YMEokEDRo00JgHgNioxsPDw+TuE1WvRb6+voiIiMi17nSze39QpRwPA4BRb3CMGTNGbBm/bNkyfP/997hx44aYj2zYsAEjR45EamoqZDIZpk+fnu230DZv3owffvgBDx48QHR0NO7cuYOxY8eKDyiHDh1q9IPEnj17ii1dN27ciPHjx+P69euIiorC69evsWnTJvTv3x9xcXGQyWRYsGCBxuDiyu/+2rVrCAoKQkREBORyuVHTe/fuLW5/7969GDFiBC5duiReT3fv3o2BAweKlYyTJ09WeyOua9euaNKkCYCM4//zzz/j4cOHiI6OxvXr1zFixAicP39e5zgW+pgjZ2zdurX4ltu6devg7e2Nu3fvIioqCg8fPsS8efPEhmr169dXqxTVd38H/isTJCUlYfjw4bh8+TIiIyMRHByMefPm4ccff9TZSEQ1l5gwYQKOHz+O0NBQscvQ7777Dr/99pvaMqYMcm5uysrXffv2QS6Xo1y5cjnytrY2MplMfIiuvCbo6mI2p8sZ2S0HzpgxA/b29pDL5Rg9ejQWLVqER48eISoqCoGBgZg1a5bYqKtIkSJq+WxWy16mykrZJ7vXEXP59ddfYW9vj9TUVAwdOhR//fUXnj9/jpiYGNy9exdTp04Vu7SrUqWKzvKCTCbDsWPH8NVXX+HWrVuIiorCgwcP1MppLVu2RPv27cVlsvP95Pbxs8Q5Zeg+pY05ymnWwBz3eW2cnZ3F8lFwcDD69++PM2fOIDIyEi9evMCSJUswZcqULN1/TVG2bFnxt3///n2t80ilUjHWly9fok+fPjh27BgiIiIQGhqKnTt3YuDAgWq/nazkatktx1m6HGhI3bp10bdvXwAZDdy+/vpr3L17F9HR0bh//z6+//577Nq1S+d3bo5ypq5ytEwmw6hRowBk/B6HDh2K06dP482bNwgPD8eNGzfw7bffimNeDh06VOd+qjbmVe294WNSqlQpjB8/HkDGW07K8QWjo6Px6NEj/Pbbb1i8eLHJ57c5ytFk/ThmlZWTSCT43//+h9q1a2Pu3Ll4//491q9fL45JlZlMJkP37t3h7e2tVkBV1blzZ/FNgJCQELUuvICMgsWCBQt0XnzLlCmDEiVKICQkBD4+PvDx8UGpUqV0vlmgy+zZs2FjYwNfX19cu3ZN7HZGlaOjI6ZPn54jre6dnJywfPlyfPXVV4iOjsaqVau0tqgtXLgwVq9eLbaakMlkmD17NsaMGYPU1FRs27YN27Zt01jO09MTBQoUwJkzZ/Dq1Sukp6fneKKlTbdu3eDn54cpU6ZoTPvkk0+watUqjTE09GnWrBn+/PNP/PTTT4iPj8eCBQu0trCqUqUKVq9erdHndlpamtiXs65uGIzRu3dv3Lp1C76+vvD19dWY3rNnT7FwoKpp06Zi/JGRkRoPUpS6dOmCGTNmqCWZa9asEVt2jRo1Sq0iDMhoHdapUyccPnwYJ06cwJ49e9TexlHq0aMHfHx8xJu3qkqVKmHVqlVG920MZLTAS05Oxrx58xASEqK160WJRIKhQ4dq7c6vfv36sLGxgVwux4wZMzBjxgz07NkTs2fPRs2aNTFt2jTMmjUL796901lh5OHhIT4sM1axYsWwceNGjBs3Di9fvsTff/+t1gJGdd1//vlnjlVo2Nraon379jh48KDWsRHq1auHpUuXapy/2YlfKpVi6dKlGDFiBEJCQrBlyxaxta8qR0dHLFy4UOMtikaNGuHQoUNITEwUB7KeN2+eWHBXvb4GBARoXOeBjN/E8OHD8fXXXxs+SCay9PU9q3r27IkzZ86ILYJzqtV9hQoVMG7cOCxbtgzJycn4/ffftbYu7NOnDy5fvoxXr15p9IGfL18+eHp64vr167hw4QJatGgBOzs73Lp1y6i3Mk1hZ2eHxo0bq3WJmfl8rFu3Luzs7MQWnKa+VQVkVAor+9r/66+/8Ndff+Hzzz/PditVY2Xn/pBZjx49cOjQIbV/6+Lk5IQNGzZg3LhxCAgIwJEjR7R2Rejg4IDZs2drVBSaysPDA2lpaThw4IA4zoeqwYMHixXtxrCxscHKlSsxYcIEXLhwASdOnBB/K6oKFy6MuXPnil3UqGrUqBFu376N0NBQ8QHo1q1bxXn1Tc+8/UuXLondeGaO09vbW2vL+yVLluC7777DhQsXsGfPHuzZs0dtesuWLSGXy/V246SNOXJGiUSChQsXYuLEiTh79iz279+v9a2HOnXqYPny5Wq/S333dwAYMmQI/v33X9y6dQsPHz7EsGHD1NZZtmxZeHt7a72H9OzZE8ePH8eFCxfw+vVrrQ/N7e3tMWrUKCxfvhwADI67lZO6du2KhQsXiq1rc+utKqUePXpgzZo1av/WJqfLGdktB1atWhVr167Fd999h8jISPFanVmJEiWwatUquLq6ip9ltexlqqyUfcxxHTGHKlWqqB3fRYsWYdGiRRrzNW7cGPPmzdPZCKJ169aIj4/H2bNntXah1rx5c7HSSyk7348ljl9un1OG7lO6ZLecZg3McZ/XpUuXLoiJicHvv/+O4OBgjfEPXVxcMGTIECxdujTb+6FP06ZN4ePjI3bdrE3z5s0xbdo0zJ49G8+fP9cYr0gmk6F///5iF3JZffaT3XKcpcuBhvzvf/9DSkoKfH19cerUKbHCWUn5NrRqt8aqslvO1FeOHj58OIKCguDn5yc25NKmR48eGD16tM59VL5VXrBgwVx9c83afPnll4iPj8eaNWtw48YNDBkyRG16yZIl0a5dO53Pt7UxRzmarB8rq/KIzp07o23btjhy5AhOnTqFBw8eICwsDHK5HAULFkSFChXQqFEjdO3a1eAAglKpFMuXL4efnx98fHwQGBiIlJQUfPrpp2jdujVGjRqlt59mmUyGtWvXYu7cubh9+zaSk5MhCAJSUlJMaoGeL18+zJs3Dz179sSuXbtw8+ZNhIeHw97eHiVLlkSrVq3Qq1cvkypSTFWrVi0cPnwYW7Zswfnz5/H8+XMkJibC2dkZZcuWRYsWLTBo0CCNFoaNGzeGj48P/v77b1y9ehVhYWGQSCRwc3ND5cqV0bVrV7H/3zNnziA2Nhbnz583avwHc+vfvz969+4tVrSkp6ejbNmy6NixI/r27av17TtDOnbsiHr16onH7eXLl0hMTISTkxOqVq2KTp06oXv37jr7jzUHZ2dn7Ny5E2vWrMHhw4fx5s0b5M+fH7Vq1cKAAQP0tkrr2LEj6tatiy1btuDcuXN4+fIl0tLS8Mknn6BmzZro1auXxsPA+/fvi90zVaxYUWtFE5DRElHZh/Lvv/+OBg0aaFQ0TJw4EW3btsWGDRsQEBAAGxsblC9fHl5eXujevXuWBtUdMmQIWrRogc2bN+Py5ctia6JPPvkE9evXR+/evTUq15TKly+P5cuXY/ny5Xjy5AkkEolaNwwDBgyAp6cntm7diuvXr+PNmzeQy+XiODUdOnRAt27dspSQu7u74+DBg9i9ezeOHz+O4OBgxMfHw9XVFZUqVYKXlxfat29vVOu8rFI+CGzcuDG2b9+OR48ewcHBAZUqVULv3r3RsWNHnQ//sxN/2bJlsX//fmzfvh1nzpzBkydPEBcXh/z586NkyZJo2rQpBg0ahKJFi2os27NnT0RFRWH37t3ib1/1DVfl9bV3797YtWsXbty4gfDwcCgUChQtWhT16tVDv379cqxVuTVc37OiRYsWKFy4MCIjI7UO0m1O48ePR40aNbB161bcvXsXsbGx4kDntWrVQt++fVG7dm38+uuv2L59u/jGg2ol0aJFizB79mxcvnwZCQkJKFy4MN6+fZutLuJ0ad26tfhgQnW8KiV7e3vUrl1bLJxlpbLKxcUFa9euxcKFCxEQEACFQpHrLeGycn/QplGjRihevDjevn2L8uXLGzzXlIO+Hzx4EEeOHMH9+/cRHR0NW1tblCpVCo0bN8bgwYPN0u99oUKFsHz5cqxduxaHDh3Cmzdv4Obmhs8++wyDBw826SGTUoECBbBu3TqcOnUKvr6+uHPnDiIjI+Hs7IzSpUujffv26N69OwoVKqR1+bFjxyI1NRWHDh1CeHg4nJ2d1caNMDRduf3Tp0/D19cXd+/eRUREBKRSKYoXL47PP/8cgwYN0tk1sZOTE9asWYOjR49i586dePToEZKSklCmTBn06NEDgwYNyvIDHXPkjMr4Tp48CR8fH7EVsoODA6pUqYKuXbuiR48eGvdhQ/d3R0dHbNq0CTt37sSBAwfw+PFjCIKAUqVKoWPHjhg2bJjOLlZsbGywevVq7NixAwcOHMCjR4+QnJyM/Pnzo1SpUvj888/Rv39/lCpVCkeOHMGTJ090NgrJDa6urmjWrBlOnToFqVQqjjmRW5T3lFu3bqm9falNTpYzslsOBDIqQU+cOIFt27bhzJkzePr0qZi/VKpUCW3btkXfvn21vjmT1bKXKbJa9snudcRc6tevj+PHj6sd38TERBQtWhQeHh7o3r07WrZsqbfBhI2NDdauXYvNmzfDx8cHL168gL29PapWrYrevXujc+fOWpfPzveT28cvt88pQ/chfbJTTrMW2b3P6zNw4EDUrVsXGzduxPXr1/Hu3TsUKlQIzZo1w/jx43H79m3z71AmX3zxhXiuPHv2TOtYcEBGzy116tTBxo0b4e/vj4iICDg7O6NevXr46quvkJycLFZWZaVMD2S/HGfpcqAhdnZ2mDdvHjp16oStW7ciMDAQMTExKFGiBDp37ozRo0dj3rx5OpfPbjlTXzlaKpXijz/+QKdOneDj44N79+4hPDxcvF7UqVMHPXv21OgOPTNlI4GOHTvm6DMxayeRSDBp0iS0aNEC//zzj3g8ixYtijZt2mDcuHFijyamMEc5mqybRDDX6JFEZDV8fHzEt4p27txpsUSE/rNs2TKxZfGFCxey9VYZmceUKVOwb98+2NnZ4d69e5YOh6yAXC5Hs2bNEBERgYkTJ4rjRhJ9CFq1aoWQkBA0adIE69ats3Q4RLnum2++wfHjx3kOfIBY9iGi7BAEAV27dkVwcDDGjx9vdJf8mZ09exZffvklAOPevKMPz+PHj9G5c2dIpVLs378flSpVsnRIRHkOx6wiIiIiQkZFckREBGxsbHKsix8iIsp90dHR+PfffwFAaxfNRET08ZJIJGJ3eT4+PhrjkQUHB2PixIlYunSp3jfq7t69K/5dvnz5nAmWrNquXbsAZLxVxYoqoqxhZRURERF99FJSUsRxN1q3bq11kG4iIsqbVq1ahdTUVBQpUiRL3ZQSEdGHrX379qhcuTLevHmD48ePq00rWLAgDh06hBUrVujstuz9+/fYsWMHgIxxl9jl2McnLi4Oe/bsgVQq1TpWGBEZh2NWERER0Ufp3r17OH36NBwcHHDs2DHcv38fUqlU74C5RERk/eLj47FixQoUKVIEt2/fxrFjxwAAo0aN+qjHjyAiIu0kEgmmTJmCESNGYMWKFejQoQOk0oz2/cWKFUPDhg1x+fJlsfFDp06dUKxYMcTExODWrVtYuXIlwsPDIZVK8dNPP1l4b8gS1q9fj4SEBPTr1w8VKlSwdDhEeRYrq4iIiOijlJKSgpUrV6p9NmrUKNSoUcNCERERkTk4Ojpi8+bNSEtLEz/z9PTE4MGDLRgVERFZs0aNGmHQoEHYvHkz9u7di969e4vT5syZgxEjRuDp06dYvXo1Vq9erbG8o6MjZs6cCU9Pz9wMm6zA+/fvsXHjRpQpUwaTJ0+2dDhEeRorq4iIiOijVKZMGZQrVw4hISEoXrw4BgwYgGHDhlk6LCIiyiapVIrGjRvj8uXLcHZ2Rrt27fDDDz9AJpNZOjQiIrJi3t7euHz5MhYuXIi2bdvCxcUFAFC8eHH4+flh9+7dOHr0KIKDgxEfH48CBQqgePHiaNGiBXr16oVPP/3UsjtAFjFv3jykpKTgjz/+gKOjo6XDIcrTJIIgCJYOgoiIiIiIiIiIiIiIiD5OUksHQERERERERERERERERB8vVlYRERERERERERERERGRxbCyioiIiIiIiIiIiIiIiCyGlVVERERERERERERERERkMaysIiIiIiIiIiIiIiIiIothZRURERERERERERERERFZDCuriIiIiIiIiIiIiIiIyGJYWUVEREREREREREREREQWw8oqIiIiIiIiIiIiIiIishhWVhEREREREREREREREZHFsLKKiIiIiIiIiIiIiIiILIaVVURERERERERERERERGQxrKwiIiIiIiIiIiIiIiIii2FlFREREREREREREREREVkMK6uIiIiIiIiIiIiIiIjIYlhZRURERERERERERERERBbDyioiIiIiIiIiIiIiIiKyGFZWERERERERERERERERkcWwsoqIiIiIiIiIiIiIiIgshpVVREREREREREREREREZDGsrCIiIiIiIiIiIiIiIiKLYWUVERERERERERERERERWQwrq4iIiIiIiIiIiIiIiMhiWFlFREREREREREREREREFsPKKiIiIiIiIiIiIiIiIrIYVlYRERERERERERERERGRxbCyioiIiIiIiIiIiIiIiCyGlVVERERERERERERERERkMaysIiIiIiIiIiIiIiIiIothZRURERERERERERERERFZDCuriIiIiIiIiIiIiIiIyGJYWUVEREREREREREREREQWw8oqIiIiIiIiIiIiIiIishhWVhEREREREREREREREZHFsLKKiIiIiIiIiIiIiIiILIaVVURERERERERERERERGQxrKwiIiIiIiIiIiIiIiIii2FlFREREREREREREREREVkMK6uIiIiIiIiIiIiIiIjIYlhZRURERERERERERERERBZjY+kASLvUVDliYpIsHYZZFSniDAAIC4uzcCRElBU8h4nyPp7HRHkbz2GivI3nMFHex/OYKG/jOZx7lMfaFHyzioiIiIiIiIiIiIiIiCyGlVVERERE9NG7fv0qrl+/aukwiIiIiIiIiD5K7AaQiIiIiD5qaWlp+Ouv5QCAVavWwdbW1sIREREREREREX1cWFlFRERElEf06NHJ0iF8kNLS0iCXywFkHGNWVpmPj89hS4dAREREREREeQC7ASQiIiKij5YgCGJFFQDI5XIIgmDBiIiIiIiIiIg+PqysIiIiIqKPVlpaGgCgefPmaNasmdpnRERERERERJQ72A0gERERUR5UquVMS4eQ5yVFPcery8thZ2eHCRMmAACuXLmC1NRUuNUaCYdCZS0bYB716sw0S4dAREREREREeQzfrCIiIiKij44gKPA+YB8AYPDgwShZsiRKliyJQYMGAQDeB/hCEBSWDJGIiIiIiIjoo8HKKiIiIiL66MS8uoqU2BAUK1YMw4YNEz8fPnw4ihUrhpTY14h5ddVyARIRERERERF9RFhZRUREREQflfTUREQEHQYAfP/993BwcBCnOTg44LvvvgMARAQfQXpaokViJCIiIiIiIvqYcMwqIiIioo/IJC9nVK1aFTY2NpDL5QgMDMRCvzhLh5WrIh4dQ3paIjw9PdGmTRuN6W3btsWePXtw8+ZNRAQfR1GPbrkfJBEREREREdFHhG9WERER5RA/Px+MHDkIfn4+lg6FCP9Ma4ytM5qjVp1acHR0hJ2dHRwdHVGrTi1sndEc/0xrbOkQc0VK7BtEv7gEqUwGb29vSCQSjXkkEgm8vb0hlUoR/fISUmLfWiBSIrIU3r+JiIiIiHLfR/dm1bNnz7Bs2TLcuHED0dHRKF26NPr27YsBAwZAKtWsu4uNjcXq1atx8uRJvH37Fm5ubmjXrh3Gjx8PJycnC+wBUe5JS0uDr+8eAEC3br1ga2tr4YgyWGNcSUmJmDt3JgBgypRpsLGxFWPs0sULBw/6AdAdr759MrS/aWlp2Lt3JwIDA1C1anV4eXWHn5+P+O+ePfvA1tZW63rS0tKwe/d2nDt3BgDQrFlL9O7dH8nJifjxxwkQBAGNGjXBkyePUbduHQwePFjvdpX72aWLF/bu3Yljx47A0dERTZs2R7589lqPha64tB2/zH/7+fkgIOAegIyHy5UquePRo2BIJBJMmTINDg6OGusHoLZuP799uH//DsLC3kMikaBp05bo06e/Whzp6QoAAgCJ1v+np6fj4cMHCAt7D1dXV0gkEoSHhyMs7D0EQcDOnVvh5dXD0M+IKEds/F8jSG2ksJHaIE4eB98IXwQkBiAxPRGOMkd4OHqgm2s3ONs6Y9OvTaCQKzBs1iVLh50jBEHA+we+AAT07tULFStW1DlvpUqV0KtXL+zatQvvH/iiZIMxWiu2iOjDs3PnViQnJ2Hr1o1ISkpCerocQUGBAIBq1aqjZ8++kMvTMHfuTAiCgAoVKuHKlYsoUqQoJkzwxqJF8xEW9h5NmrRAjx69xHxFIpGgSpVqSE+X4+LF8wAAV1dXABJIpVJIJBJUrlwNMllGuTQ9XYEHD+7h5cvnyJ/fCfPnL4a9vaPWvFCZlz14cB8AULlyVchkNv+/HvX4vbx6aM3HlHldxYruePQoSMynMue1fn4+atuRSCQICgpE1aoe4rqVuZNMJjMq/zUmXzbkv7wtHenpCgQHB4q5MADs3bsLgYH3UbWqB3r27KtzG9ZY1jAXa9g3a4iBiIiIrNNHVVn18OFDDBw4EPHx8ahTpw5q1KgBf39/zJw5E7dv38aCBQvU5o+Pj8egQYMQFBSEcuXKoUWLFggICMCGDRtw/vx57NixA87OzhbaG6Kcd/ToIWzfvhlAxhgeXbp0s2xA/88a41qyZCHu3bsDAFi6dCGqVashxvjkyWNcvXoZgO549e2Tof09evQQdu3aBgC4d+8Onj9/Km7v3r07cHLKjy5dumldz9Gjh7Bnzw5xXXv27ECBAgVw8uRxhIa+AwD4+u4V1+Xs7IwWLToY3O7Tp4/h75/xd0JCPHx8dus8Frri0nb8VNer+rnS/ft3xb+XLl2IyZOnaaxfECD+W3V9Snv37kDBggU04jDW+/ehGp8lJyeZtA4ic9k0vQkgAyLSIrDyzUqciDqBFCFFbZ4TUSew6s0qtC3UFl9/+jXcbN2waXoTDPntgoWizjnxb+8gKfIpXFxc8NVXXxmc/6uvvsKxY8cQE/kE8e/uwLl4rZwPkogsTnnflsvl2LVrq9q0+/fvwsnJCffv3xNzP2X+ERr6DpMmfYfo6EgAGTnF69cv1HIN1VwF0MwbMk9XSkhIwPTpU9GqVTuteaFqXqZvPffv38WLF8/EmFRzL9W8TklfXpt5Oxn54DON/MyY/Fc1J8tqfq8tb1PmwoIA8bvM+MxJ5zassaxhLtawb9YQAxEREVmnj6YbQEEQ8OOPPyI+Ph5//PEHtm/fjuXLl+PYsWOoXLkyDhw4gGPHjqkts3jxYgQFBaFPnz44fPgwli5dimPHjsHLywuPHz/G4sWLLbMzRLkgNjZGrXC+c+dWxMbGWDCiDNYYV0jIK7VCub//ZezY8V9BWXWatnj17ZOh/Y2NjcHOnVvU1pf5AcGOHVsQEvJKYz0hIa/U4lTauvUfvHz5XOu+rlu3DrGxMQa3m7kCSNs8yhi0xaX6ma71Zt7PzPz9LyMoKFBtXTt2bFGLW1ec27dv1oiDKK/Z+L9GgAx4lvwMgx8OxsHIgxoVVUopQgoORh7EkIdD8DT5KSQyScbyHxCFPAVhDw8AAMaNG4cCBQoYXKZgwYIYN24cACAs8AAUcu3Hj4g+Ltu2bdKZhygrqpR05RpZ8eLFc2zfvkn8tzIv1JaX6aMakzL30rW8vrxWG23Tjcl/M8dkan6feX2qtm/frJHz7tixRes2rLGsYS7WsG/WEAMRERFZr4+msurixYsICgpC/fr14eXlJX5euHBh/PLLLwCAzZv/S2BjY2Oxe/duODk5YfLkyWIXgTY2Nvjll19QsGBB7NmzB4mJibm7I0S5ZMeOLUhISBD/nZCQgJ07Lf/g3hrjmj9/tsZnSUna36TRFq++fTK0vzt2bDF4HUpMTMT8+bM11jN//mytcaak6H4Yq9y+Mds1RBmDtrhUP8uO33//RW1diYmJRsWdlJRk1jiILEFqI0VEWgTGPRqHCHmEUcuEy8Mx7tE4hKeFQ2rzYaWJkU9OQ54cg6pVq6Jr165GL+fl5YUqVapAnhyDyKdncjBCIsorkpOTrWLb5sjLlLmXvuV15bWmbMNQ/mtofkP0rS8pKUljHxITE7VuwxrLGuZiDftmDTEQERGR9fognkL4+flhwIABqFOnDj777DN88cUXWL16tVoif/58Rp/gbdq00Vje09MTrq6uuHHjBuLj4wEA165dQ3JyMj7//HONsany58+Phg0bIjk5GdeuXcvBPSOyjBcvnuPYscManx87dhgvX76wQEQZrDGuK1cumbxt1Xj17ZO//yW9+/vixXMcPXrIqG1qizGrx+zo0UNGb9cQc8aljfKanhXm/k2Zs2U1kSH/TGsMG6kNVr5ZaXRFlVKEPAKr3qyCjdQG/0xrnEMR5q7UhHBEPfsXAODt7Q2ZTGb0sjKZDN7e3gCAqKf/IjXRtONJRHnLixfPLR2CSY4dO5ztvCw38mhj8l9d8xtizPq0OXr0kNo2rLGsYS7WsG/WEAMRERFZtzxdWaVQKDBp0iT8+OOPuH//PmrXro2mTZvi/fv3+PPPPzFgwADExsYCAB4/fgwAcHd317qucuXKQaFQ4MmTJ2rzV6pUSev85cuXBwAEBQWZdZ+ILE0QBGzcuBaCIGhMUygUOqd9jHEpFAqsXLkkS8tt3LhWb9zKdeuatmHDWqxfvzpLcWeXJb7/D8GKFYugUCgsHQZ9JGxsbBAnj8OJqBNZWv541HHEp8fDxubDGN40LPAABEU6OnfujM8++8zk5WvWrIlOnTpBUMgRFrg/ByIkImugzDfzkrySWxiT/2qb39B8+soIhgiCgA0b1kAQBKssa5iLNeybNcRARERE1i9PV1Zt2bIFBw8eRNmyZXH48GGsW7cOK1aswKlTp9CiRQsEBASIXfy9f/8eAFCkSBGt61J+Hh4eDgAICwszav6ICLaupQ/LjRtXcefOLZ3Tb9++iRs3cv+NQmuMa+fOrYiPj8vSsrdv38SuXVv17lNcnO5137lzU23wa7J+8fHxagOfE+WUSV7OUEAB3whfnWNUGZIipMA33BcKKDDJy9nMEeauhLCHSHgfAEfH/Pjmm2+yvJ5vv/0Wjo6OSAgNQEIYGysRfYgM5ZuUPbdv38TOnfrz38zzG8rvs/ud3blzCzduXLPKsoa5WMO+WUMMREREZP3ydHPZjRs3AgDmzp2LkiVLip87OTlhwYIFaNmyJY4cOQJvb2+xj2p7e3ut61J+ruyrW/l/BwcHo+Yn+hCkpaVh48a/Dc63ceNa1KxZG7a2trkQlXXGlZSUCB+f3dlax9692Vue8p5du7axwopyXNWqVSGTyBCQGJCt9QQkBkAmkaFq1aqA31UzRZe7BIUc7x/4AQBGjx4FNze3LK/Lzc0No0aNwtKlS/H+gS/KNp0EiTRPp9K5okePTpYOgYisiKn5s7783tgygiHr16+GRCLJVizWyhrKUdYQAxEREeUNefbNqrdv3yIkJASffPIJateurTHd2dkZzZo1gyAIuHbtGqTSjF3VlYQqXzlX/t/U+YmIiIisgbLrvsT07DWoUS7/oXQFSERERERERETWK89WVim79StRooTOeZRvW4WHh8PR0REAkJycrHXelJSMbnKU8xk7v643r4jyIltbWwwbNsrgfMOGjc7VFm/WGJeDgyN69OidrXX07Jm95YmItJHL5QAAR5ljttajXF65vrxIIrVB0WpeAIC1a9eK3T1nRVhYGP7+O6NleNFq3fhWFRFRFpiaP+vL740tIxgyYsRXGD58dLZisVbWUI6yhhiIiIgob8izpWzlG036XtdPT08HANjZ2aFo0aIIDAxEeHg4KlSooDFv5jGqihYtCgA6H2oYGtOKKK/y9KyPmjVr6+xTvFatOvD0rJfLUVlnXH37DsSRIwezNG5VrVp10KfPQAQFPdS5T87OzjrHrapZsw4UinSOW5WHODk5YePGHeKbu0RZYUyXaoGBgahVpxY8HD1wIupElrfl4eiBdCEdgYGBWV6HNchfpAryF/VAwvsALFu2DDNmzMjSepYtW4bExETkL+aB/EUqmznKD5ePz2FLh/BBKVIkYwy5sLCsjZlJ+gmCgN9++x/HrcohtWrVQd++AxEcrDv/zTy/ofzeUBnBkJo1a4vbsLayhrlYQznKGmIgIiIi65dnn5gpK5NevXqlcx7lNFdXV1SqVAkA8PjxY435BEHA06dPIZPJxIosffMDwJMnTwAAlSvzYQV9WCQSCYYP/1JrRbBUKsWwYaON6tP9Y4hLKpVi3Ljvs7TcsGGjIZVK9e7T119/r3Pa8OGjMXLkmKyEnW2W+P4/BOPGTWBFFeWKhX5xkEKKbq7dkE+SL0vryCfJh25u3SCFFAv98v5D8SJVv4BEaoNDhw7hzh3TK/nv3LmDw4cPQyK1QZGqXXMgQiKyBsp8My/JK7mFMfmvtvkNzaevjGCI6rLWWNYwF2vYN2uIgYiIiKxf3shstfj0009RokQJhIaG4tYtzdY5cXFxuHjxIqRSKerWrYumTZsCAE6dOqUx782bNxEZGQlPT084OTkBAOrVqwd7e3tcvnwZiYnqYz4kJCTg8uXLcHR0hKenZw7sHZFllS5dBu3ba7beb9++E0qXLmOBiDJYY1wNGjQ0eduq8erbpwYNGurd39Kly6BDh85GbVNbjFk9Zh06dDZ6u4aYMy5tlNf0rDD3b6pBg4ZmXR+RPnK5HM42zmhbqG2Wlm9XqB2cZE55ugtAVXb53VCoXHMAwIIFC8S3742Rnp6O+fPnAwAKlW8BO0fXHImRiKyDJXPdrGjfvlO287Lc2Gdj8l9d8xtizPq06dChs9o2rLGsYS7WsG/WEAMRERFZtzxbWQUAQ4cOBQBMmTIFr1+/Fj9PSEiAt7c34uPj0a5dOxQtWhT169dHpUqVcPHiRezatUucNzIyUuwOZvjw4eLnjo6O6NatG2JiYjBjxgzxYY1cLsdvv/2G2NhY9O3bN1sPQomsWb9+g5A/f37x3/nz50ffvgMtGFEGa4zL2/snjc90jWenLV59+2Rof/v1GySOsaeLo6MjvL1/0liPt/dPWuPMl0/3mxjK7RuzXUOUMWiLS/Wz7Pj55xlq63J0dDQqbgcHB7PGQZTbhs68CLlCjq8//RquNqZVrrjZuGHsp2MhV8gxdObFHIow9xWu0Ao29gURGBiI/fv3G72cn58fHj58CBt7FxQu3zIHIySivMLe3t4qtm2OvEyZe+lbPrvjNBuT/xqa3xB963NwcNDYB0dHR63bsMayhrlYw75ZQwxERERkvfJ0ZdXgwYPRsWNHPH/+HJ06dcLIkSMxfvx4tG7dGmfOnEG1atXw66+/Ash4tXz27NlwdHTEtGnT0KdPH4wfPx4dOnRAUFAQ+vTpg1atWqmtf8KECShXrhx8fX3RoUMHfPvtt+jQoQN8fX1RrVo1fPPNNxbYa6LcUaBAQfTp81/BoW/fgShQoKAFI8pgjXGVKFEK9ev/99ZMgwYN0a/fYPHfqtO0xatvnwztb4ECBdG37yC19aluD8goFJYoUUpjPSVKlFKLU2ngwKEoXbqs1n0dOXIkChQoaHC7ut4iynwsdMWl+pmu9Wbez8waNGiIypWrqq2rX79BanHrirN//8EacRDlNQq5Am62blhRaYXRFVZuNv8/v60r0tOMf/soL5Da5EORKl8AAFasWIHY2FiDy8TExGDFihUAgCJVu0Bqk7VuFYnowzJgwBCdeYiLS2G1f5vzzeoyZcqif/8h4r+VeaG2vEwf1ZiUuZeu5fXltdpom25M/ps5JlPz+8zrU9W//2CNnLdfv0Fat2GNZQ1zsYZ9s4YYiIiIyHrZWDqA7JBKpVi0aBGaNWuGXbt24ebNmwCAsmXL4ssvv8SgQYNgZ2cnzv/ZZ59h9+7dWLp0Kfz9/fHo0SOUKVMGEydORO/evTXW7+Ligh07dmD58uU4efIkzpw5g+LFi2PUqFEYM2YMW9zTB69Dh85ITk76/7+7WDia/1hjXN99Nwlz5878/7+9IZPJxBi7dOmGgwd9AeiOV98+GdrfDh06IyEhHoGBAahWrTq6du0BP7+94r+Vy2hbT4cOnREXF4tz584AAJo3b4UOHbqgefOW+PHHCRAEAY0bN8Xjx49Qr54nunfvjqioJJ3bVe7nF190w549O3Ds2BE4OjqiWbOWsLOz03osdMWl7fh98UU3HDjgK37u57cXAQH3AGT0hV+pUmU8ehQEiUSC777z1rp+QRDEf3/xRTf4+vrg/v07CAt7D4lEIh4D1WXT0xWQSABBgNb/y+VyPHz4AGFh7+Hq6gaJRILw8DC8fx+q9fsmyg3DZl3CpulNUN6+PDZX2YxVb1bheNRxpAgpGvPmk+RDu0LtMPbTsXC1dQXSgeG/X7ZA1DnLqXhNOLy8hOjIp1i9ejW8vb31zr969WrExMTAoXAFOH1SM5eiJCJr0qfPQCgU6Xj48AEAwMOjBjp06ILWrdth7tyZEAQBlSpVwqVLF1GkSFFMmPAjFi36A2Fh79GsWUt0794Lvr4Z+YpEIkGVKh5IT5fj4sVzAAA3tyIAII6bVKWKB2QyKQRBgEKhQEDAPbx8+Rz58zth5sw/kC9fPq15oTIve/DgPgCgSpVqkMlkEARoxO/l1UPMp1RzHmVeV6mSO4KD/8unMue1fn571bYjkUjw8OEDtXxQmTtJpVKj8l/VHC+r+f1/eVs6FAoFgoICxVxYEAQkJCQgMPC+Wn5sKC5rKWuYizXsmzXEQERERNZJIgiCYOkgSFNqqhwxMUmWDsOsihRxBgCEheX9gdqJPkY8h003YEBPJCcnwd7eAdu27bV0OPQB6NHjv7EeSrWcadQyG//XCFIbKWykNohPj4dvuC8CEgOQmJ4IR5kjPBw90M2tW8YYVQo50tPSP8iKKqWU2Dd4cWERpDIptm3diooVK2qd79GjRxg4cCAUAlCm8QTkK1A8lyPNu16dmSb+7eNz2IKRfHh4L84dvH9TTuE5TJT38Twmytt4Duce5bE2RZ5+s4qIiMia9e07EPv3+6Br1x6WDoU+YsNmXQIA/DOtMfLb5Ef/ov0hk8jE6elCOqSQIi0t7YMao0qXfAU+hUuZRoh+cRHz58/HX3/9BYlEojaPIAiYP38+FAoFXMo0YUUV0UeG928iIiIiotzHyioiIqIc4uXVA15efNBF1kFZETXJyxlVq1aFjY0N5HI5AgMDsdDv42pV5lqpPeLe3MKNGzdw8uRJtG3bVm36iRMncPPmTcjs8sPVvZ2FoiQiS+H9m4iIiIgo97GyioiIiOgjstAvDvC7aukwLEpm5wjXyp3w/v4eLF68GE2aNIGDgwMAICkpCUuWLAEAuLp3hMzW0ZKhEhEREREREX0UpJYOgIiIiIgotxUsVR/5CpRAaGgoNm7cKH6+YcMGhIaGIl+BkihYqr7lAiQiIiIiIiL6iLCyioiIiIg+OhKJFEU9ugMANm/ejNevX+P169fYvHkzAKCoRzdIJEyViYiIiIiIiHIDuwEkIiIioo+SQ6GycC7hibiQG1i0aBEEQUBaWhpkMhkcCpW1dHhEREREREREHw1WVhERERHlQa/OTLN0CB8EQRAAAGfPnhU/s7W15fElIiIiIiIiykXs24SIiIiIPloSiQQ2Nv+137KxsYFEIrFgREREREREREQfH1ZWEREREdFHTVlBlbniioiIiIiIiIhyB0vjRERERHmEj8/hbC1fpIgzACAsLM4c4XxQrl+/CgCoW7e+hSMhIiIiIiIi+viwsoqIiIiIPnqspCIiIiIiIiKyHHYDSERERERERERERERERBbDyioiIiIioo/E9etXxS4PiYiIiIiIiKwFuwEkIiIiIvoIpKWl4a+/lgMAVq1aB1tbWwtHRERERERERJSBlVVEREREZJV69Ohk6RA+KGlpaZDL5QAyji0rq8zLx+ewpUMgIiIiIiLKs9gNIBERERHRB04QBLGiCgDkcjkEQbBgRERERERERET/YWUVEREREdEHLi0tDQDQvHlzNGvWTO0zIiIiIiIiIktjN4BEREREZPXWeVWwdAh50ki/J0hPT0d6ejrs7OwwYcIEAMCVK1eQmpqKyY2Lw93N0cJR5l0j/Z5YOgQiIiIiIqIPAt+sIiIiIiL6QAmCIL5BNXjwYJQsWRIlS5bEoEGDAAD/3HgDhYLdARIREREREZFlsbKKiIiIiOgDlZ6eDkEQUKxYMQwbNkz8fPjw4ShWrBieRSXj32dRlguQiIiIiIiICKysIiIiIiL6IMWnyMW3qr7//ns4ODiI0xwcHPDdd98BAHbeDUV8arpFYiQiIiIiIiICWFlFRERERPRB2nP/PQDA09MTbdq00Zjetm1b1KlTB3Ep6dh7LzS3wyMd8ueXoWDBfMifX2bpUIiIiIiIiHINK6uIiIjIJH5+Phg5chD8/HwsHQoR6fAyOhknHkdCKpXC29sbEolEYx6JRAJvb29IpVKceByJl9HJFoiUAMDFxR5ubk4o7OYIR0dH2NnZwdHREYXdHOHm5gQXF3tLh0h5AO/PRERERJSX2Vg6gNz27NkzLFu2DDdu3EB0dDRKly6Nvn37YsCAAZBKNevu2rRpg1evXulcX0BAAGxsPrrDSERmkpaWBl/fPQCAbt16wdbWNs9v25j1GprHlNiMWdfevbsQGHgfVat6wMurBw4e9NOYX3U9Xbp4ifOo/t2tWy/I5WmYO3cmFAoFqlb1gK2trcH9TE9PByCBTCZFt269AOD/P1cAEAAA6ekKBAcHomrV6ujZsw9sbW31xuTjsxvnz/8LAHB1dYNUKoWHRw14efXAnj07cOzYEaSlpaJChYqoXr0mbG1t0aWLF/bt24Nz585AoVBAIpGgSJGiqFKlKh49Coa7exUAEjx4cA8vXjwHIKBMmTKQSKR4//49oqOjIJFIxG7FduzYAi+vHjq/GyKyDEEQ8M+NNxAEoE+f3qhYsaLOeStVqoRevXph165d2HTzLX5uWVZrxRbljIIF80FqI4WN1AZx8jj4RvgiIDEAiemJcJQ5wsPRA91cu8HZ1hmFXPm9kG5xcTHYtGkdBEHAtm2b0KnTF2IO0aFDJ8ybNxvPnz9Bamoq7OzyoX37TujVq69aPtG0aQv06TNAzHUA4IcfpuDo0cNITU1FUFAgJBIJpkyZBgcHR408xc9vn0a+lZ6uQHq6HAEBd/Hy5UtIJEDp0mVQpYoHHj0KgkQiEbehXI9qTE2aNIONjZ1aDrV3704EBgagUiV3PHwYiIiIcDRp0hx9+gxQy8eSkhIxe/YMhIeHoWnTFujevZfWHFCVvrwyc07Zs2ffbOWohvyXR2Ycw+DghzrzRHPl8pnXCSDL27BkOYeIiIjypo+qluXhw4cYOHAg4uPjUadOHdSoUQP+/v6YOXMmbt++jQULFqjNHxcXh9evX8PNzQ0NGzbUuk5tFVxERMY6evQQtm/fDCBj/JAuXbrl+W0bs15D85gSmzHr2rVrKwDg3r07eP78Ga5evawxv+p6nj59DH//yxp/Ozg44P79e7h37w4AICDgntH7qeTg4ABBgMbnSvfu3YGTU3506dJNZ0xPnjwW9wEA3r8PFeNR3T8AePgwEA8fBmqsQyks7D0ePLgvbjuzwMBArXECQEoK38IgskZXXsUiMCwRLi4u+OqrrwzO/9VXX+HYsWN48D4G/q9i8XnpgrkQJRUq5ADIgIi0CKx8sxInok4gRUhRm+dE1AmserMKbQu1xdeffg03WzfIJOwekDRNmzYVgpDRACYtLVUth7h48TxevnwuziuXy7Fv326EhLxWyxn27t2JggULquU606ZNVVsWAJYuXYjJk6epbUM1N7l37w5evHimkXMoBQY+QGDgA/Hf06dP/f9GMpo5jo/PbvFvZQ61a9c2cTuZY1fNx5YsWSjmanv27MDLly+05oCq9OWVmXNKJyenbOWohmjLI3XliebK5TOvUzVnNXUbliznEBERUd4kEZQZ7QdOEAR4eXkhKCgIf/zxB7y8vAAAkZGRGDZsGIKCgrB06VK0b99eXObq1asYPHgwevfujVmzZuVqvKmpcsTEJOXqNnNakSLOAICwsDgLR0JkHWJjYzBu3CgkJCQAAPLnz48VK/5GgQI5/5AwK9s25hw2Zr2G5jElNmPW9fXXI5GYmKg1XuX8ANTWo4uDgwOSkjSvzcbsp5KjoyMA6IxJOc+8eYswZcpEgzFZ2saN23PlN0vmkdfuxT16dBL/XudVwYKR5B3JcgV+OBSMyCQ5fv75Z3Tv3t2o5Xx8fDB79mwUdrDBgs7usLdhgyxjjPR7Iv7t43PY6OUKFswHqa0Uz5KfYdyjcYiQRxhcxs3GDcsrLUd5+/JIT0tHTEyKwWXo4xAUFIipUyepfWZvb4/kZNMbleTLlw8pKYZ/W3PmLMSsWdNzNU9xdHSEIAhaczEgI09btWo9ChQoiJCQV/jmG92V9abmqNpySkdHR6xcuc6kHLVChZIADN+HdeWRyu1mzhPNUY7IvM3MOasp27BkOYcop+W1fJqI1PEczj3KY22Kj6YUevHiRQQFBaF+/fpiRRUAFC5cGL/88gsAYPNm9VZLDx5ktPby8PDIvUCJ6KOxY8cWtQJoQkICdu7cmqe3bcx6Dc1jSmzGrEtfpZBy/szr0UXXwxFj9lMpMTFRb0zKeebPn231FVUAcu03S0TG2f8gDJFJclStWhVdu3Y1ejkvLy9UqVIFkUly7H8QloMREgBIbaSISIswuqIKAMLl4Rj3aBzC08IhZWUiqfj99180PstKRRUAoyqqlNvM7TwlMTFRZy4GZORpyrxk/vzZetdlao6qLadMTEzMco5qiL7cVFueaI5cPvM2M+espmzDkuUcIiIiyrs+iFKOn58fBgwYgDp16uCzzz7DF198gdWrV6sl6OfPnweQMQZVZp6ennB1dcWNGzcQHx8vfq7s+oiVVURkbi9ePMexY5otsI8dO4yXL1/kyW0bs15D85gSW1bXpW3+o0cPGZzPmPUY2k9T5PTvwFyOHj2UZ2Il+tCFxqXg4MNwAIC3tzdkMuO7i5PJZPD29gYAHHoYjtD41ByJkQAXF3vYSG2w8s1KoyuqlCLkEVj1ZhVspDZwcbHPoQgpLzlwwFetDJtbLLFNYxw5chAHD/oZlZsYm6P6+1/Smddpy4P0revZs2cG4zImj9S2f9nJ5U3Jmw1tw5LlHCIiIsrb8nRllUKhwKRJk/Djjz/i/v37qF27Npo2bYr379/jzz//xIABAxAbGwsAePz4MQDA3d1d67rKlSsHhUKBJ0/+68rjwYMHkMlkePbsGQYOHIi6deuiXr16GDNmDO7evZvzO0hEHyRBELBx41po64VVoVDonGbN2zZmvfrWr1AosGHDGqNjM7S9DRvWYMMG4/ZFoVAYs4tGrcfQfn6IBEHA+vVrPpr9JbJmW26/g1whoHPnzvjss89MXr5mzZro1KkT0hQCttx6mwMREgDY2NggTh6HE1EnsrT88ajjiE+Ph43NRzX8MGmRnp6OTZvWWToMq7N+/Wqj5jM2R12xYonOPEcQBGzYsMboHHXZsmV6cyZ9yxu7P6Yua8o2DW3DkuUcIiIiyvvydGXVli1bcPDgQZQtWxaHDx/GunXrsGLFCpw6dQotWrRAQECA2MXf+/fvAQBFihTRui7l5+HhGa1RU1NT8fTpU6Snp+PHH39ESkoKGjRogEKFCuHMmTMYMGAAjh49mgt7SUQfmhs3ruLOnVs6p9++fRM3blzLU9s2Zr27dm3VO8+dO7eMjs3Q9u7cuYW7d3VPzynG7OeH6O7dWzn2myUi49x5G4cbIXHI7+iIb775Jsvr+fbbb+Ho6IgbIXG485b9uOcEBRTwjfBFipC1MadShBT4hvtCAfM0tqC8a/Hi+UhPT7d0GHmaMblbfLz+a+GdO7eMzlGvXbuGy5cv65xuaHlDspLLm7pNfduwZDmHiIiI8r483Rxv48aNAIC5c+eiZMmS4udOTk5YsGABWrZsiSNHjsDb21vs29reXnt3GcrPlX0yBwUFQS6X//9AoCvQsGFDte3OmTMHU6dOhaenp84KMCKizNLS0rBx498G59u4cS1q1qwNW1tbq9+2sevdu3e3UevTZ+PGtfDwqG7U9izFHPuZF82e/aulQyD6aMnTFdh0M+NNqFGjR8PNzS3L63Jzc8OoUaOwdOlSbLr5FvM65IeNLE+3b8s1PXp0MjjPsWPHIJPIEJAYkK1tBSQGQCaRIX9+Gdq3b5+tdRF97MyRu23YsMboHHX58uVYsGC5Rq5tbE5tiCm5fFa3qW0bliznEBER0Ychz5Y83759i5CQEHzyySeoXbu2xnRnZ2c0a9YMgiDg2rVrkEozdlUikWhdn+pr+wBQo0YNXLhwAQcOHFCrqAKAYcOGoU2bNkhMTMS+ffvMuVtERERERPSBUnbdl5iemK31KJdnV4BERERERPShyLOVVcpu/UqUKKFzHuXbVuHh4XB0dAQAJCcna503JSWjGw7lfEBG14C61t+yZUsAwP37902MnIg+Zra2thg2bJTB+YYNG2321oY5tW1j19uzZ2+j16nLsGGj4eDgaNT2LMUc+0lEZAobmRRD6hQHAKxdu1bs1jorwsLC8PffGS3jh9QpzreqzEwulwMAHGWOBubUT7m8cn1ElHXmyN2GD//S6Bx1/PjxWnNtY3NqQ0zJ5bO6TW3bsGQ5h4iIiD4MebYpnvINKF1vSgEQ+++2s7ND0aJFERgYiPDwcFSoUEFj3rCwMAC6x7TKTDmfrsovIiJdPD3ro2bN2jr7c69Vqw48PevlqW0bs94+fQYiKOihznlq1sx4S9aY2Axtr2bN2hAE5Pq4Vcbs54fos89q45dfZum9J5N1KFLEGQAQFpY3xiIypls1ylCzuDM8SzjjRkgcli1bhhkzZmRpPcuWLUNiYiI8SzijZnFnM0f5YfPxOWxwnoSEdORzSIeHowdORJ3I8rY8HD2QLqQjISHdqO3Shyk9PR39+nXjuFXZYEzu5uTkrHfcqpo1axudo9arVw8NGzZEeHi81umGljckK7m8qdvUtw1LlnOIiIgo78uzTSWLFi0KAHj16pXOeZTTXF1dUalSJQDA48ePNeYTBAFPnz6FTCYTK7IOHz6MSZMm4cCBA1rX/fr1awDAJ598kvWdIKKPkkQiwfDhX2p9sC+VSjFs2Ogce+ifU9s2Zr1SqVTvPMOHf2l0bIa2N3z4lxgxQvt0bfObgzH7+SGSSCRGH2siylmDan0CW6kEhw4dwp07d0xe/s6dOzh8+DBspRIMql08ByIkAJBCim6u3ZBPki9Ly+eT5EM3t26Q5t2iHJmJTCbD0KHW+7a5pYwY8ZVR8xmbo44b973OPCdzTmooR/3mm2/05kz6ljd2f0xd1pRtGtqGJcs5RERElPfl2RLOp59+ihIlSiA0NBS3bmm22omLi8PFixchlUpRt25dNG3aFABw6tQpjXlv3ryJyMhIeHp6wsnJCQAQERGBgwcPYvv27RrzC4KA/fv3AwCaNGlizt0ioo9E6dJl0L695hsD7dt3QunSZfLkto1Zr6F5TIktq+vSNn+HDp0NzmfMegztpyly+ndgLh06dM4zsRJ96Io550PnKm4AgAULFpj0tkV6ejrmz58PAOhcxQ3FnOxyJEbK6LrP2cYZbQu1zdLy7Qq1g5PMiV0AEgCgSxcvsQybmyyxTWN07NgFXbp4GZWbGJujNmjQUGdepy0P0reucuXKGYzLmDxS2/5lJ5c3JW82tA1LlnOIiIgob8uzlVUAMHToUADAlClTxDedACAhIQHe3t6Ij49Hu3btULRoUdSvXx+VKlXCxYsXsWvXLnHeyMhIsZuU4cOHi5937twZTk5OuHHjBjZu3Ch+LggCVqxYgdu3b8Pd3R2tWrXK4b0kog9Vv36DkD9/fvHf+fPnR9++A/P0to1Zr6F5TInNmHWpjkWYmXL+zOvRxcHBQe969MWm5OjoqDcm5Tze3j8ZFZOl5dZvloiM07VaERR2sEFgYKDYuMoYfn5+ePjwIVwdbdG1mnHdYlPWREcnQ66Q4+tPv4arjatJy7rZuGHsp2MhV8gRHc3uyCnDzz9rdvtpb2+fpXXly2fcG38//zwj1/MUR0dHnbkYkJGnKfMSb++f9K7L1BxVW07p6OiY5RzVEH25qbY80Ry5fOZtZs5ZTdmGJcs5RERElHfl6cqqwYMHo2PHjnj+/Dk6deqEkSNHYvz48WjdujXOnDmDatWq4ddffwWQ8cr57Nmz4ejoiGnTpqFPnz4YP348OnTogKCgIPTp00et4qlw4cKYPXs2bG1tMWfOHHTp0gXffvstOnTogGXLlqFIkSJYtmwZbGzy7LBfRGRhBQoURJ8+/xXa+vYdiAIFCubpbRuzXkPzmBKbMevq23eQ2jL16zfUmD/zeho0aKj17/79B6stb8p+KvXrN0gjJm3zlChRSmdM2mIwZprqOswlt36zRGQcexspBv5/F34rVqxAbGyswWViYmKwYsUKAMDAWp/A3iZPFxHyBIVcATdbN6yotMLoCis3m/+f39YV6Wkco4j+U7lyVZQuXVbts/79h4h/Z56mpC1nGDhwqNrn2pZt0KAhKleuqpanZF6XKTlHmTL/bUNfHtOv3yD06zdY5/T+/QeLeUmJEqU01qUtB1SlL6/UllP26zcoyzmqIbrySOV2M+eJ5sjlM28zc85qyjYsWc4hIiKivCtP17RIpVIsWrQIzZo1w65du3Dz5k0AQNmyZfHll19i0KBBsLP7rwuTzz77DLt378bSpUvh7++PR48eoUyZMpg4cSJ69+6tsf727dujRIkSWL16Na5du4bnz5+jaNGiGDx4ML7++msULlw41/aViD5MHTp0RnJy0v//3eWD2LYx6zU0jymxGbOuhIQEBAbeR7Vq1dG1aw8cPOirMb/qer74ohsOHPDV+LtDhy5o3bod5s6dCYVCgWrVqsPGxsbgfqanp0MikUAqlaJDhy4QBOH/P1dAIsl4a1ehUCAoKBDVqlUX16crpi5dusHHZxfOn/8XAODmVgRSqRQeHjXQtWsP7NmzHceOHUFaWioqVKiEGjVqwsbGBl980Q0+Prtx7twZKBQKSKVSFClSFJUrV8OjRw9RuXJVAEBAwD28ePEcgIAyZcpCIpEiLCwUUVFRkEgkSEtL0/udEJFlfV6qAE4WcURgWDRWr14Nb29vvfOvXr0aMTExqFY0PxqUKpBLUX7cYmJSUKiQA8rbl8fmKpux6s0qHI86jhQhRWPefJJ8aFeoHcZ+Ohautq6wkdggKjbJAlGTNZs5cw6GDu0v/ls9P+qMefN+x/PnT5Camgo7u3zo2LELevToo5ZPNG/eSi3XAYAffvgJR48eRGpqKoKCAiGRSPDdd94a2+jSpRv8/HzEfMvLqwcOHPBFeroCCkU67t+/g5cvX0IiAcqUKYcqVaohOPghJBIJvL1/wpEjB8X1qMbUtGlz2NjYquVQCQnxCAwMQKVKlREUFIjw8DA0a9ZSIx/77rtJmD17hji9W7deWnNAVfryysw5ZXZzVEP+yyMzjqG+PNFcuXzmdSpz1qxsw5LlHCIiIsqbJIIgCJYOgjSlpsoRE/NhFUKLFHEGAISFxVk4EiLKCp7DpDRgQE8kJyfB3t4B27bttXQ4ZIK8dh736PHfmBfrvCpYMJK852V0MqYeewyJRIpt27ahYsWKWud79OgRBg4cCAgKzG5fEaVdstZ12MdqpN8T8W8fn8MmL1+wYD5IbaSwkdogPj0evuG+CEgMQGJ6IhxljvBw9EA3t24ZY1Qp5LCRZrQ1zCvnMOUu3p+tX167DxORJp7HRHkbz+HcozzWpsjTb1YRERFR7uvbdyD27/dB1649LB0KEelQ2sUebSsWxvFHkZg/fz7++usvSCQStXkEQcD8+fOhUCjQvlJhVlRZQExMxptULi72yG+TH/2L9odMIhOnpwvpkEKKtLQ0REcnZ6nARx8P3p+JiIiIKC9jZRURERGZxMurB7y8+CCMyNr1ql4Uxx9F4saNGzh58iTatm2rNv3EiRO4efMmnPPJ0LNGMQtFSQAQHZ0s/p0/vww2NjaQy+VISODYVGQ83p+JiIiIKC/j6MlERERERB8gp3w2sLW1BQAsXrwYSUn/dTGdlJSEJUuWAAD6flYMTnYyreug3JeQkI6YmBRWVBERERER0UeFlVVERERERB8omUwGiUSC0NBQbNy4Ufx8w4YNCA0NRblC9mhRrpDlAiQiIiIiIiICK6uIiIiIiD5YEolEfLtq8+bNeP36NV6/fo3NmzcDAIZ6fgqpVKJvFUREREREREQ5jmNWERERERF9wGQyGWQyGVJTU7Fo0SIIgoC0tDQ0LesCdzdHS4dHRERERERExMoqIiIiIrJ+I/2eWDqEPM3W1hbp6ek4e/as+Nm1d8m4zuNKREREREREVoDdABIRERERfeAkEglsbP5rp2ZjYwOJhN3/ERERERERkXVgZRURERER0UdAWUGVueKKiIiIiIiIyNJYSiUiIiIiq+Tjc9jSIXxwrl+/CgCoW7e+hSMhIiIiIiIi+g8rq4iIiIiIPhKspCIiIiIiIiJrxG4AiYiIiIiIiIiIiIiIyGJYWUVERERElIuuX78qdsdHREREREREROwGkIiIiIgo16SlpeGvv5YDAFatWgdbW1sLR0RERERERERkeaysIiIiIiKtevToZOkQPjhpaWmQy+UAMo4vK6tylo/PYUuHQEREREREREZgN4BERERERLlAEASxogoA5HI5BEGwYERERERERERE1oGVVUREREREuSAtLQ0A0Lx5czRr1kztMyIiIiIiIqKPGbsBJCIiIiKDGs6sZ+kQ8rSY5zG4sfwO7OzsMGHCBADAlStXkJqaimoj3VGwbEELR/jhuDztmqVDICIiIiIiIhPxzSoiIiIiohwkKAQE73sCABg8eDBKliyJkiVLYtCgQQCAYN8nEBTsDpCIiIiIiIg+XqysIiIiIiLKQW+uvkNcSDyKFSuGYcOGiZ8PHz4cxYoVQ9zreLy5+s5yARIRERERERFZGCuriIiIiIhySFpiGp4efgYA+P777+Hg4CBOc3BwwHfffQcAeHrkOdISOX4VERERERERfZxYWUVERESURfnzy1CwYD7kzy+zdChkpZ4de4G0RDk8PT3Rpk0bjelt27ZFnTp1kJaQhmfHX1ggQiIiIiIiIiLLY2UVERGRhfj5+WDkyEHw8/OxdChkAhcXe7i5OaGwmyMcHR1hZ2cHR0dHFHZzhJubE1xc7C0dIlmJ+DfxeH3pDWQyGby9vSGRSDTmkUgk8Pb2hlQqRcilt4h/m2CBSInIHHhfJyIiIiLKOhtLB0BEZE3S0tLg67sHANClixcOHvQDAHTr1gu2trbZXgcAcZrqOlWXyTyf6jqyGpMp0tLSsHfvTgQE3IMgCJBKpahWrQbGjBmpc3uqy4SFvYerqxs8PGpAKpUCUD6cFf7//xLIZFKN+I09Brr2OykpEXPnzgQATJkyDTY2tti7dxcePLiH9PR0SCSS/9+X6ujZs6+4/vT0dKSmpuDEiWPInz8/Zs6ci+XLFwMAvv/+ByxevEBcJwDMnTsTgiCgUqXKCA5+CEEApFIJKleuBplMitTUFBw9ehipqSkoXLgwihQppvKAWgJBUEAmk6FatRrw89uLlJRk7Ny5FV5ePUz6nij3FSyYD1IbKWykNoiTx8E3whcBiQFITE+Eo8wRHo4e6ObaDc62zijkKoFCrkBMTIqlwyYLEQQBwb5PAAHo1asXKlasqHPeSpUqoVevXti1axeCfR+j9pjPtFZsEZF127lzK5KTk/DPP39jz54dWLp0FeztHfD77zPw4sVTODg4omjRopBKZahcuSpkMmVxXAAgQXq6HMHBD1G1anX07NkHcnka5sz5DWFh79GkSQv06dMfALB37y4EBt6Hu3uVTOsAlHlWly5e8PHZg/Pnz0AQBLi6uiIyMhJFixaDt/dUHDp0APfu3cHL/2PvvsOjKvP+8b/nzEwgk4QASQABadKS0IusjwtWirTEgHQUVoVHsoh+2Vhwdy27Ks8PVBABsSxZasJCTDCEBFwVsQBKCUmYhN6CpgFpkzLl/P6IM0wm0zOTmUner+vyMjnnLp/7ngnzOXOfcvUyRBEYP/4xTJ8+y2y+VVWlwrvvvoXCwgKEhoZCEKQN8il9HeP4wsMjTXIuHQARUqm0QW6n0ajxz3++jqtXLyMgIBCjRz+EmJjpSE1NgVarhU6nQ16e0tCmuVzQNJd0R57sTu6K3x3t+vpcExERkXfiYhWAixcvIioqCs899xyWLFlitey6devw0Ucf4dChQ+jUqVMTRUhETSU9fR927twKALhw4TyOHfsJQN1zRSZPjna4jYsXz+Po0TttiCIM+4zbNK5jWs64DdP27I3JEenp+7Br145627KzT6NTpxA88cQTdtUpLCyAUpljtR/T+K3NgT2vxdq17yErKxMA8OGH7yEiYiB27dreoFx29mkEBgbWa1+vsrICL7wQi6oqFQDgL395Hrdu3TK0KYow9JGdfbpBu6aKi4tRXFxsdvzG5aurq8yWIe/Rrp0/IAVK1CXYcGMDDt46iBqx/kLUwVsHsfHGRoxtNxZLOi9BqDwU7dr549Ytvr4tUWFmEW5fLEXbtm2xePFim+UXL16MjIwM3L5QisLMYnQcEtYEURKRKxl/nldWVuAvf1mGPn364cyZrN+3VaK4uAiA+bxBLysrE4GBAcjOzjKU27MnAcHBbSCKMOQ3+pzEHOOcEQCKigoB1OVof//7q7hy5XK98l98sRs3buSbzTPXrn3PEEdhYYEhftN8Sp+/GcdnKecyzWmzs7MMuWNlZSX27EnAtWtXDPlf/bkJNJsLmuaS7siT3cld8bujXV+fayIiIvJOElEURdvFmq+bN29i3rx5uHDhApYtW2Z1seqrr77CsmXLoNFo3L5YVVurQWlp8/pyKywsCABQVFTu4UiIzCsrK0Vs7DOorGx4C6aAgACsX/8Z2rQJdroNhUIBAFCpVPXaBFCvjmk5S+yNyRFlZaVYsuRps30HBAQgISEBarXU7jrWGMdvOm/W5sDcuPPzr2Hp0vpfBrdu7W9xEcjf3x8SicThmN0pKSnN0yGQBcHBrSDIBVyqvoTYc7Eo0ZTYrBMqC8VHfT5Cr9a9oFVrveYKK0c/i2NiJhp+vu8fI90SU3OkrdHiyP/3M2pKa/Haa6/h8ccft6teUlIS3nnnHbQK9sMfXhoJaSs+C80ZP/3tZ8PPze3fVubT3s3438zGat26Naqrqxtsk0gkqKpy/3GiPt8qLy9rkGPpmeZTCoUCoijWi8+enMvf39+hMSkUCmzY8Hm9XNA0l3RHnuwKlv6G3RW/O9r1lbkmchd+FhP5Nv4NNx39XDuiRT+z6uzZs5g9ezYuXLhgs+z27dvxwgsvQKPRNEFkROQJCQnbzC4yAXVneCYmNrxKx5E2VCpVvQN1fZumdUzLWWJvTI5ISNhmse/Kykps3rzZoTrWGMfvyByYG/eqVe80KGftaqWqqiqvWqgi7ybIBJSoS+xeqAKAYk0xYs/FolhdDEHWotOtFuny11dRU1qL8PBwTJ061e56UVFR6N+/P2pKa3Hlm2tujJCIvJ3pQpV+W1MsVAF38i1zOZaeaT6lUqkaxGdPzuXomFQqVYNc0DSXdEee7E7uit8d7fr6XBMREZH3apHfnlRVVeGjjz7CjBkzcPnyZXTt2tVi2QsXLmDRokV46623EBgYiICAgCaMlIiaypUrl5GRYf3s64yMNFy9eqVRbZhKT9/ncB1HYnLElSuXkZ6+z2qZ5OTkev3ZU8eajIw0HDnyo8NzYDzuI0d+dNkcEJlq27Y1ZIIMG25ssHuhSq9EU4KNNzZCJsjQtm1rN0VI3kZVXIWr314HAMTFxUEqtf/qKKlUiri4OADA1W+vo6qkeV1lT9ScbdnyL0+H4HLp6fu8Nscyjs1SDu7KPNmd3BW/O9r19bkmIiIi79asFqtSUlIwZ84cDBs2DIMGDcKUKVOwadOmBmel7d+/H+vWrUNgYCA++ugjREdHW2zzjTfewKFDh3D//fcjKSkJbdu2de8giKjJiaKI+PhPYeuuqDqdzmI5e9swV68xd2O1FpOjcWze/Ild/W3e/Kkhbnvq2Gpvw4a1DsevH7dWq8WGDWsbFYO3aOF35fVaMpkM5ZpyHLx10Kn6B24dQIW2AjIZHxPaUpz/8iJErYhJkyZh0KBBDtcfPHgwJk6cCJ1Gh3N7L7ohQiJyNY1Gg+Tk3Z4Ow+W8OTfR56HWcmFX5cnuZO0YojHxu6Ndd8VKREREpNcsFqt0Oh2WL1+Ol156CdnZ2Rg6dChGjx6NwsJCvP/++5gzZw7KysoM5du2bYulS5ciIyMDY8eOtdr2gAEDsHHjRvzrX/9C586d3T0UIvKA48ePITPzpF1lT506gePHf26w3ZE2XM1STI44fvwYTp8+ZVfZzMy6/hypY01FhXP3CT516gTWrFnldH1v09jXkNxDBx2SS5JRIzr3zKkasQbJxcnQQefiyMgbleTeRHFOCRQBCixdutTpdp5//nkoFAoU55SgJO+mCyMkInd4/fVXPR1Ci5SZeRKJidut5uCuyJPdydYxhLPxu6Ndd8VKREREpNcsTvPdtm0bUlNT0aNHD3z++eeG2/pVVFRg+fLl+Pbbb/H666/jgw8+AAA8/PDDePjhh+1q++WXX3Zb3ETkeWq1GvHxnzlUJz7+UwwePBRyudzpNlzNNCZHqNVqbN78qUN1Nm/+xCvOnPzhh+88HYLLvPPOG54OgUxkZGRAKpEiR5XTqHZyVDmQSqQICJBi/PjxLoqOvI1Oo8O5lLrnoD77zLMIDQ11uq3Q0FA888wz+PDDD3Eu+QLaLW/LZ585KSZmoqdDICI32rNnl80yjcmT3cneYwhH43dHu+6KlYiIiMhYszjqjY+PBwCsXLmy3vOnAgMDsXr1agQFBWH//v24ceOGhyIkIiIiX6O/dZ9Ka/3B8Lbo6/NWgERERERERERE5vn8YtWvv/6K/Px8dOrUCUOHDm2wPygoCGPGjIEoivj5Z16STkT1yeVyLFjwjEN1Fix4tt7Zgs604WqmMTlCLpdj4cJnHaqzcOEi/OlPi5zqz5Xuv3+Mp0OgZkyj0QAAFFJFo9rR19e3R82TIBPQJ+oeAMCnn36K4uJip9sqKirCZ5/VncHeJ/oeXlVFRGTBtGkzbJZpTJ7sTvYeQzgavzvadVesRERERMZ8/hTfwsJCAECXLl0sltFfbdWYLw2IqPkaPvxeDB481K5nTg0ZMgzDh49sVBuuZikmRwwffi8GDRpi1zOoBg++05+9dawJDAxy6rlTQ4YMwwsvxCEz82SzeG7Vnj37IJFIPB0GGams1KKVvxaRikgcvHXQ6XYiFZHQilpUVmqRlJTmwggdFxYWBAAoKrLvb4a3UHNMSP/2CI0MQXFOCdatW4c333zTqXbWrVsHlUqF0MgQhPRr7+IoWxZP/825mqN/w9Q0NBoNZsyY6ukwWpzBg4di5sy5OHs212IO7oo82Z1sHUM4G7872nVXrERERER6Pn+apv6ZKda+4NNqtQAAPz+/JomJiHyLRCLBwoWLbC4UCIKABQueNVvO3jbM1WvMAoW1mByN409/WmxXfwsXPmuI2546ttqLjX3B4fj145ZKpYiNfaFRMXgLLlR5JwECokOi0UrSyqn6rSStEB0aDcH3Uy6yU+8pvSDIBOzbtw+ZmZkO18/MzERaWlrdlVpTe7khQiJyNZlMhscff8LTYbicN+cm+ty7Ljc1n4O7Kk92J2vHEI2J3x3tuitWIiIiIj2f/+akQ4cOAIBr165ZLKPfFxIS0iQxEZHv6datO8aPt34FwfjxE9GtW/dGtWFqwoRJDtdxJCZHdOvWHRMmTLJaJjo6ul5/9tSxZvz4iRg16j6H58B43KNG3eeyOSAypdFoECQLwth2Y52qP67dOARKA3kLwBZEEeqPux+ou+J/9erVhpOm7KHVarFq1SoAQLcHu8I/xN8tMRKR682fv9DTIbjchAmTvDbHMo7NUg7uyjzZndwVvzva9fW5JiIiIu/m84tVnTt3RpcuXVBQUICTJxtejl5eXo4ffvgBgiBgxIgRHoiQiHzFrFnzEBAQYHZfQEAAZs6c26g2FAoFFIo7z77Rt2lax7ScJfbG5IhZs+ZZ7DsgIAALFzb8IsZaHWuM43dkDsyNOy5uRYNyrVtb/pLX39/fqZip5bl9uxoanQZLOi9BiMyxk15CZaF4rvNz0Og0uH272k0Rkjfq8XA3tAr2g1KpxN69e+2ul5KSgtzcXLRq2wrdH7rbjRESkbdr3bq12W3+/k2ziK3Pt8zlWHqm+ZRCoWgQnz05l6NjUigUDXJB01zSHXmyO7krfne06+tzTURERN7L5xerAOCpp54CALzyyiu4fv26YXtlZSXi4uJQUVGBcePGGa7CIiIyp02bYMyYcedA69577zP8PHPmXLRpE+xwG6NG3Wlj1qx5mDlzXoM2TeuYljNuw/hne2NyRJs2wfX6Nvb000+jbdu2DtWxxjh+W3Ng67Xo0uXuemVGjboPs2fPt9j37NnzLcbs73/nC5V27drVa9O4D2o5dBodQuWhWN9nvd0LVqGy38vLQ6BV239lDTUP0lZS9J5Sdwu/9evXo6yszGad0tJSrF+/HgDQe3IvSFtJ3RojEblfu3btnc4d5sx5skHdOXOexKxZlvMbY8Y5o6nu3XvYrKPPt0xzLGOm+dSsWfMaxGcp5zLua/bs+Wb7sNTvrFnzGuSCprmkO/Jkd3JX/O5o19fnmoiIiLyXzNMBuML8+fNx8uRJ7N+/HxMnTsTIkSPh7++PX375Bbdu3UJERATeeOMNT4dJRD5gwoRJqK6uAgBMnhyN1NTk37dPdqqNKVOi8eWXd9oQRdGwz7hN4zqm5YzbMG3PHSZMmITKygrk5GRBFEUIgoDIyIF4/PHH7apTVFSI0NAwREYONDzbShQBiaTuOYMSiQSCIDSI39oc2PNaLFu2HCtX/uP3n+MglUpRWVmJM2eyoNPpDLFERg6s175Wq4VaXYMDBzIQEBCAf/7z/7Bu3QcAgBdffAkffPD/GdoURR1WrvwHRFFE3779kZenBFB3D//+/SMhlQqoqalGenoaamtr0L59CDp06Fjv/v3Gc5qYuN3Rl4c8oLS0Bu3a+aNX617Y2n8rNt7YiAO3DqBGrGlQtpWkFca1G4fnOj+HEHkIoAXKymo9EDV5WofBYcj/8VfcvngbmzZtQlxcnNXymzZtQmlpKdreE4wOg0ObKEoicpeAgEC8995HaNXKD2+//SauXLkIf38FOnToCEEQ0L9/BKRSqVGOBOh0WuTlKRERMQATJkzGI4+Mw7vvvoWiokKMGfOQIX+prKyEUpmNfv3CTdq4k2dNmRKNPXv+g8OHvwEAtG8fgps3S9ChQ0e89NJrSE1NQVZWJq5evQxRBB57bDKmTZthNs9ctmw53n33LRQWFiA0NMyQx5jLbY3j04/jTs6lg0QCQ3zGfT3yyDj885+v4+rVywgICMQDDzyM6OjpSE1NhlarhSiKyM09Y2jTHNNc0te4K353tOvrc01ERETeSSKKoujpIFxBFEV88cUX2LVrF/Ly8gAAPXr0wJQpUzBv3jz4+flZrLtu3Tp89NFHWLZsGZYsWWK1n4cffhj5+fk4dOgQOnXq5NIxGKut1aC0tMpt7XtCWFgQAKCoqNzDkRCRM/g37Hpz5kxDdXUVWrf2x44dezwdDtkQHNwKgkyATJChQluB5OJk5KhyoNKqoJAqEKmIRHRodN0zqnQaaNVar1uocvTvOCbmznMp7vvHSLfE1JxV3KjAsQ9OQCpIsX37dvTu3dtsuXPnzmHu3LkQIWLki8MQeJf528mSfX7628+Gn5OS0jwYievxs9i78XOdbOHfMJHv498xkW/j33DT0c+1I5rFlVVA3VntMTExiImJcbju0qVLsXTpUrvKfv311w63T0REZM7MmXOxd28Spk51/LOLml5pad2VVG3btkaALACzO8yGVHLnVm1aUQsBAtRqNZ9RRQCAwM6B6Po/nXH9hxtYtWoVPv7443pXWgJ1J1ytWrUKOp0OXf/YmQtVRD6Mn+tERERERM5rNotVREREviYqKgZRUfxCy9cYL0QFBEghk8mg0WhQWclnU1FDPcd3R8HJQhw/fhxfffUVxo4dW2//wYMHceLECcgD5Og5rruHoiQiV+DnOhERERGR8wRPB0BERETkqyortSgtreFCFVkkV8jRa2JPAMCaNWtQVXXnNs9VVVVYu3YtAKDXYz0gV8g9EiMRERERERGRp3GxioiIiIjIjTrf2wlBXQJRUFCA+Ph4w/bNmzejoKAAQV0D0fle9z0LlYiIiIiIiMjbcbGKiIiIiMiNJIIEfR+/BwCwdetWXL9+HdevX8fWrVsBAH2je0MiSKw1QURERERERNSs8ZlVRERERERuFtwjGJ2Gd8BvxwvxwQcfQBRFqNVqdBreAcE92ng6PCIiIiIiIiKP4mIVEREREdn0099+9nQIPk8URQDAoUOHDNtu55RxbomIiIiIiKjF420AiYiIiIiagEQigUx251wxmUwGiYS3/yMiIiIiIiLiYhURERERURPRL1CZLlwRERERERERtWQ8QiYiIiIis5KS0jwdQrP0yy/HAAAjRtzr4UiIiIiIiIiIvAMXq4iIiIiImhAXqYiIiIiIiIjq420AiYiIiIiIiIiIiIiIyGO4WEVEREREREREREREREQew8UqIiIiIjLrl1+OGZ6vRERERERERETkLnxmFREREfm0mJiJng6hWRJFETU1NQCAVq1aQSKReDii5i0pKc3TIRARERERERF5DK+sIiIiIqIGNBoNRFGEKIrQaDSeDoeIiIiIiIiImjEuVhERERFRPaYLVPqFKyIiIiIiIiIid+BtAImIiKjZWDVsmKdDaBa2nD2LY9XVeOCBByCKIr777jsMCgrCk337ejq0ZiXuxAlPh0BERERERETkFbhYRUREREQGF8vKcKyoCH5+fnjxxRcBAEeOHMGxoiL8sVMn9GrTxsMREhEREREREVFzw9sAEhEREREAQCeK+M/FiwCA+fPno2vXrujatSvmzZsHAPjPxYvQ8XaARERERERERORiXKwiIiIiIgDAjwUFuFZZiY4dO2LBggWG7QsXLkTHjh1xrbISPxUUeC5AIiIiIiIiImqWuFhFRERERKhUq/HllSsAgBdeeAH+/v6Gff7+/li2bBkA4MsrV6DSaDwSIxERERERERE1T1ysIiIiIiLsu3YNlRoNhg8fjkcffbTB/rFjx2LYsGGo0Giw7+pVD0RIROR+AQFSBAe3QkCA1NOhEBERERG1KFysIiIit0pJScLTT89DSkqSp0MhIgvyKytx+NdfIZVKERcXB4lE0qCMRCJBXFwcBEHA4d9+Q35lpQciJaLG4udyQ23btkZoaCDahyqgUCjg5+cHhUKB9qEKhIYGom3b1p4OkYiIiIio2ZN5OgBPunjxIqKiovDcc89hyZIlZsvk5uZi48aNOHbsGFQqFe6++25MnToV8+bNg0KhaOKISa1WIzl5NwAgOno65HK5z/TnTFtNVceV9e1tz1Y/xvsnT45CamoKtFodABFSqdSwzVx9c3Xt/dm0raoqFVau/Ad0Oh369euPs2fzIIoiIiMHYtq0mU7Pb22tGkplNkpKivE//zMagiDF2bNKhIcPQFTU42bjqapS4d1330JhYQFCQzsgPDwSUqkUUqnQYG737ElETk4WRBEQBAkiIgYY4lWr1di1awe+//4QwsI6IC7uVaSnp5mdCwD4z3924tChr1FVpULv3r3x4osvG8pPmDARK1f+A+fPn4NMJkf37j0gk8nQr184RFGHH344jMLCAoiiiMTE7YiKinFovojI/URRxH8uXoQIYPr06ejdu7fFsn369MH06dOxa9cu7L54Ec8PGGB2YYuIvJNarcb27fHQaDTYtm0zfv75CAoLC3DzZgkkEgl69eqNGzeuIyAgEO++uxoZGek4cyYLWq0WUqkU/ftHQBAEaLVanDmTZfj8Hz/+MUilMpw9m4s+ffri7Nk86HQ6AHUL3eHhkRAEKQARWq0WeXlKw35BENCvXwSkUgFA3b8nWq0Wubk5kEgk6N8/AqIo/l5HhEQCREYORFRUDFJSkqBU5iA8fAAmTZqM1atXAgD+8pdXrOY2+jzxmWcWQJAJkAkylGvKkVySjBxVDlRaFRRSBSIVkYgOiUaQPAjtQiTQaXQoLa2xe66Tk3dDq9UaxqXPY53NsT19bOLN/RARERGR72uxi1U3b97En//8Z9TW1los8/XXX2PZsmWora1F586dMWTIEFy9ehXvvfce0tPT8emnnyIkJKQJo6b09H3YuXMrgLrnZ0yeHO0z/TnTVlPVcWV9e9uz1Y/x/osXz+Po0Z/q7b9w4TyOHfvJbH3jusbljNuxtN20rbVr30NWViYAICcny7A9JycLgYGBjZpfvS+++I/h56ysTFy+fNHs2NaufQ/Z2acBAIWFBThz5k48pnO7a9eOen1kZ582xJuevg979iQCAAoKfsPf/vYqrl69bHYuRBHYvTvB0M6pU6fqlf/hh8OGn9VqNXJzzxj6M1VdXWXXHBFR0zpRXIzzZWVo27YtFi9ebLP84sWLkZGRgXOlpThZUoJhoaFNECURuUJ6+j5ofn/mXN2CU3a9/efO5QEAKisrsXz5Mty+fbPefnOf73WLEXsMv+vzJmPGOZQ55tq1ti8nJwtXrlwy5CxZWZk4cuQHQ05inKuY5oyiCOzcuRXffPMNIAVK1CXYcGMDDt46iBqx/kLUwVsHsfHGRoxtNxZLOi9BqDwU7dr549Yt2zmNuZxPz9kc29PHJt7cDxERERH5vhZ5G8CzZ89i9uzZuHDhgsUyt2/fxksvvYTa2lo8++yz+Oqrr7Bx40bs27cPr732GnJycvDmm282YdRUVlaKXbu2G35PTNyOsrJSn+jPmbaaqo4r69vbnq1+TPebLlQBMHzpYFrftK5xOeN2LG03bis//1q9cqYSErY1an4tMTc2W7EYz21i4jaL8ebnX0NCQv0vTvRf5gD15yIhYZvZL1mMyxv/TES+p0arxReXLwMAYmNj0aZNG5t1goODERsbCwBIunQJNVqtO0MkIhexNw/RM12o8jam+aGl/MQ4f0pI2IbExG3473//C0iBS9WXMD93PlJvpjZYqNKrEWuQejMVT+Y+iYvVFyGRShAc3MpqbLbm2pkc29PHJt7cDxERERE1Dy1qsaqqqgofffQRZsyYgcuXL6Nr164Wy+7fvx/l5eUYNmwYli9fDqn0zgN2n3zySYwePRoZGRlQKpVNETqh7uCy0uj5GJWVlUhMtP+A25P9OdNWU9VxZX1727PVj+l+W4zrO1rXWlurVr1jtaxKpWrU/DoSj61YjOdWpVJZjHfVqndQVWXfFU4qlYpXQxE1cweuX8ft2lqEh4dj6tSpdteLiopC//79cbu2FgevX3djhETkKo3NkZoDlUoFlUoFQSagRF2C2HOxKNGU2FW3WFOM2HOxKFYXQ5BZP4y2NdfO5NiePjbx5n6IiIiIqHloFotVKSkpmDNnDoYNG4ZBgwZhypQp2LRpE6qrq+uV279/P9atW4fAwEB89NFHiI6Ottjm2bNnAQAPPfSQ2WcxjBo1CgBw+PBh1w2ELLpy5TIyMtIabM/ISMPVq1e8uj9n2mqqOq6sb2976en7rPZjqZ4tGRlpOHr0R6fqmmvryy+T7Rp3evq+Rs2vPfbvT7Wrj4yMNKSn77Naxh1/L44wd5UcEXlGUVUV/pufDwCIi4urd2KOLVKpFHFxcQCAr27cQLFJzkVE3qUxeUhz8/XXX0MmyLDhxga7F6r0SjQl2HhjI2SCDG3btjZbxt65diTH9vSxiTOa+viNiIiIiHyfTy9W6XQ6LF++HC+99BKys7MxdOhQjB49GoWFhXj//fcxZ84clJWVGcq3bdsWS5cuRUZGBsaOHWuzbQAICAgwu18mq3vcl7VbCZJriKKI+PhPIYpig306nc7iPm/oz5m2mqqOK+s70p4oihb72bz5U2ze/IlTr6dOp8OGDWtd8l7Q6XTYsuVzu8qKomgzZmvz4Ur6f7e82fr1H/hEnEQtQdLly9CIIiZNmoRBgwY5XH/w4MGYOHEiNDodki5dckOEROQKTZWH+AqZTIZyTTkO3jroVP0Dtw6gQlthOB405shc25tje/rYxBlNffxGRERERM2DTy9Wbdu2DampqejRowfS0tLw+eefY/369fjvf/+LBx98EDk5OXj99dcN5R9++GH8+c9/trgAZaxnz54AgF9++cXs/uPHjwMAbt707nu5NwfHjx9DZuZJi/tPnTqB48d/9sr+nGmrqeq4sr6j7VmSmXkCp0+fcrieXnl5udN1TWkdeAZLZubJRs1vS1JRUYFdu3Z4OgyiFu/MrVvIunkTAQoFli5d6nQ7zz//PBQKBU7fvIkzt265MEIichXmIXesWbMGOuiQXJJs8RlVttSINUguToYODU++cXSu7cmxPX1s4oymPn4jIiIiouah4elgPiQ+Ph4AsHLlynrPnwoMDMTq1avx0EMPYf/+/YiLi0Pnzp0danvixIl4//33kZaWhv/5n//BE088YdiXlJSEgwfrzsSrra1t/EDIIrVajfj4z2yWi4//FIMHD4VcLvea/pxpq6nqNDZOa+xtr7nZvPmTRs1vS7Jr1w4uWBF5kEanw+6LFwEAzzz7LEJDQ51uKzQ0FM888ww+/PBD7L54ESuGDoVM8OlzoTwmJmaip0MgavbCw8MhlUiRo8ppVDs5qhxIJVIEBEhRWVl3gpOzOZ+1HNvTxybOaOrjNyIiIiJqPnz224Rff/0V+fn56NSpE4YOHdpgf1BQEMaMGQNRFPHzz46ftdWhQwe89dZbEAQBf/3rX/HYY48hNjYWU6ZMwYoVKzBnzhwAMHv7ByIiIqKWxtwzPomIvIn+2E2lVTWqHX19HgsSEREREbmOzy5WFRYWAgC6dOlisYz+aqvi4mKn+oiOjsaWLVtw//3347fffsNPP/2EkJAQbN68GVOmTAEAtGnTxqm2yT5yuRwLFjxjs9yCBc+65Kw8V/bnTFtNVaexcVpjb3vNzcKFixo1v0RETUUmCJjeqxcA4NNPP3U6TwKAoqIifPZZ3Rn003r25FVVROTVNBoNAEAhVTSqHX19fXuA8zmftRzb08cmzmjq4zciIiIiaj589lQw/QNZrZ3Fq3/mjJ+fn9P9jBw5EiNHjmywfffu3QCAu+66y+m2yT7Dh9+LwYOHWrzv+ZAhwzB8eMPXyBv6c6atpqrjyvqOtmfJ4MHDIIo6p59bFRQU5LLnVkmlUrufWzV48NBGzW9LEhgYiPj4BAj8QptciLdPc1xEu3YY2L49sm7exLp16/Dmm2861c66deugUqkwqH17RLRr5+IoW5akpDRPh0A+ICwsCABQVGR/viOKIt5666/MQwAolUoMGTYEkYpIHLx10Ol2IhWR0Ipawy0A9RzN+ezJsT19bOKMpj5+IyIiIqLmwWe/LezQoQMA4Nq1axbL6PeFhIQ43H5FRQWOHTuG3Nxcs/uPHDkCABgwYIDDbZNjJBIJFi5cZHZhUhAELFjwrEtvPeTK/pxpq6nquLK+I+1JJBKL/Sxc+Cz+9KfFTr2egiBgyZIXXPJeEAQBTz1l35mx1sbqSBlX8IUFoNjYF30iTqKWIKZHD8gEAfv27UNmZqbD9TMzM5GWlgaZICCmZ083REhErtBUeYgveOGFFyBAQHRINFpJWjnVRitJK0SHRkMwcyjtyFzbm2N7+tjEGU19/EZEREREzYPPfmPYuXNndOnSBQUFBTh5suEZW+Xl5fjhhx8gCAJGjBjhcPuFhYWYP3++2TONi4uL8dVXXyEwMBCjR492Kn5yTLdu3TF+fMMz58ePn4hu3bp7dX/OtNVUdVxZ3972JkyYZLUfS/VsGT9+IkaNus+puubamjw5yq5xT5gwqVHza4/HHptsVx/jx0/EhAmTbMbhSaNG3efR/onojjB/fzzSuTMAYPXq1XZfTQrUXb2+atUqAMCjnTsjtHVrt8RIRK7RmDykudFoNAiSBWFsu7FO1R/XbhwCpYH1bgFozN65diTH9vSxiTOa+viNiIiIiHyfzy5WAcBTTz0FAHjllVdw/fp1w/bKykrExcWhoqIC48aNM1yF5YhevXqhZ8+eOHHiBDIyMgzbKyoqsHz5clRVVWHhwoUICgpq/EDILrNmzUNAQIDh94CAAMycOdcn+nOmraaq48r69rZnqx/T/bYY13e0rrW24uJWWC2rUCgaNb+OxGMrFuO5VSjMP4dBoVAgLm4F/P397epboVCgdWv7yhKRbxrXtSva+vlBqVRi7969dtdLSUlBbm4u2vn5YezvzwglIu/W2BypOVAoFJg8eTI0Og2WdF6CEJljd+AIlYXiuc7PQaPT4PbtaovlbM21Mzm2p49NvLkfIiIiImoefHqxav78+Xjsscdw+fJlTJw4EU8//TT+/Oc/45FHHsE333yDiIgIvPHGG063/84770Amk2HZsmWYO3culixZgkceeQRHjhzBI488gsWLF7tuMGRTmzbBmDHjzsHNzJlz0aZNsE/050xbTVXHlfXtbc9WP6b7zV2Nc++9d7YZ1zeta1zOuB1L243b6tLl7nrlTM2aNa9R82uJubHZisV4bmfOnGcx3i5d7sasWfPrbe/WrYfhZ+O5mDVrHmbPrl/WtLzxz0Tke1pJpXi8Rw8AwPr161FWVmazTmlpKdavXw8AeLxnT7SSSt0ZIhG5iL15iF7btu3dGE3jmeaHlvIT4/xp1qx5mDlzHnQaHULloVjfZ73dC1ahst/Ly0OgVVu/EtXWXDuTY3v62MSb+yEiIiKi5kHm6QAaQxAEfPDBBxgzZgx27dqFEydOAAB69OiBRYsWYd68efDz83O6/WHDhmHLli1Yv349Tp48CUEQ0LNnTyxfvhwxMTGQyXx6+nzShAmTUF1d9fvPk32qP2faaqo6rqxvb3u2+jHeP2VKNL78MhlarQ4SSd3f/uTJ0UhNTTZb37iucTl9O9a2m7a1bNlyrFz5D+h0OvTvH468vLrn2EVGDmzU/NbWqpGbm4Pi4iLcf/8YCIKAvDwlIiIGYOrUGLNjW7ZsOd599y0UFhYgLKwDwsMjIZVKIQhCg7mtrKxATk4WgLrnBhjHO2HCJJSVleL77w8hLKwD4uJeQ3p6qtm5EEUR5eVlOHToa1RVqdCnTx+88MIrhvITJkzCypVv4fz5c5DL5ejevSekUin694+AKOrw/fffoaDgN4fniYiazrDQUBz+7Tecv30bmzZtQlxcnNXymzZtQmlpKfq0aYOhTjwXlIg8Z8KESdi8+RPD7xERA1BYWICbN0sgkUhwzz19kJ9/DQEBgVi58n2kp6fhzJks6HQ6CIKA8PBICIIAjUaDM2eycf78WchkckyYMBFSqQx5eUr06dMPZ8/mQqfTGZ5LFBExABKJAImk7jZ8eXlKw36JRILw8EjDz6II6HQ6KJXZkEgk6N8/EoCI3NwzEEXRkNdERcUgOXkPlMocREQMwKRJU7Bq1bsA6q6O37+/LlcxzRlFUcRnn8Vj0aI/oVfrXtjafys23tiIA7cOoEasaTBnrSStMK7dODzX+TmEyEMALVBWVmvXXFdXV0Gr1RrGpc9jnc2xPX1s4s39EBEREZHvk4iiKHo6CGqotlaD0tIqT4fhUmFhdbdMLCoq93AkROQMZ/+G58yZhurqKrRu7Y8dO/a4IzRq4WJi7jwTY9WwYR6MxHflV1Zi5alTEKRSbN++Hb179zZb7ty5c5g7dy4ginh58GB0aeG3FGusuN9PtAKApKQ0D0ZCvsIV+TQ/l+8IDm4FQSZAJshQoa1AcnEyclQ5UGlVUEgViFREIjo0uu4ZVToNtGqtXQtVRJbwmJjI9/HvmMi38W+46ejn2hG8NIiIiNxq5sy52Ls3CVOnxng6FCKyoEtAAEbfdRe++/VXrFq1Ch9//LHhigg9URSxatUq6HQ6PHDXXVyoIvJR/Fy+o7S07kqqtm1bI0AWgNkdZkMquXNrU62ohQABarXa6jOqiIiIiIio8bhYRUREbhUVFYOoKH4hRuTtJt19N44XFeH48eP46quvMHbs2Hr7Dx48iBMnTiBQJsOkbt08FCURNRY/lxsyXogKCJBCJpNBo9GgstL6s6mIiIiIiMh1BE8HQERERESeFyCXY0r37gCANWvWoKrqzu2Iq6qqsHbtWgDAlO7doeBzO4momaqs1KK0tIYLVURERERETYyLVUREREQEAPifjh1xd0AACgoKEB8fb9i+efNmFBQU4O6AANzXsaPnAiQiIiIiIiKiZomLVUREREQEABAkEjzRqxcAYOvWrbh+/TquX7+OrVu3AgCe6NULgsmzrIiIiIiIiIiIGov3cCEiIiIig15t2uDesDAcKyrCBx98AFEUoVarcW9YGHq1aePp8IiIiIiIiIioGeJiFRERETUbcSdOeDqEZkEURQDAoUOHDNtOl5dzfomIiIiIiIjILXgbQCIiIiKqRyKRQCa7c06TTCaDhLf/IyIiIiIiIiI34WIVERERETWgX6AyXbgiIiIiIiIiInI1fvNAREREPi0pKc3TIfiMsLAgAEBRUbld5X/55RgAYMSIe90WExERERERERERF6uIiIiIyCwuUhERERERERFRU+BtAImIiIiIiIiIiIiIiMhjuFhFRERERERERNTC/fLLMcMtgImIiIiaGm8DSERERERERETUgqnVanz88UcAgI0bP4dcLvdwRERERNTScLGKiIiIiIiIiHxCTMxET4fQLKnVamg0GgB1c8zFKvdKSkrzdAhEREReh7cBJCIiIiIiIiJqoURRNCxUAYBGo4Eoih6MiIiIiFoiLlYREREREREREbVQarUaAPDAAw9gzJgx9bYRERERNRXeBpCIiIiIiIiIfM6s+5Z5OgSfV1B6HWnHt8HPzw8vvvgiAODIkSOora3FQxHT0DG4q4cjbD4Sflrr6RCIiIi8Gq+sIiIiIiIiIiJqYXSiDkfPHgQAzJ8/H127dkXXrl0xb948AMDRs19BJ+o8GSIRERG1IFysIiIiIiIiIiJqYc7dOI2S8gJ07NgRCxYsMGxfuHAhOnbsiJLy33DuxmnPBUhEREQtCheriIiIiIiIiIhakBp1FY5fPAQAeOGFF+Dv72/Y5+/vj2XL6m6xePziIdSoqz0SIxEREbUsfGYVEREREREREVELcvLSYdSoqzB8+HA8+uijDfaPHTsWu3fvxokTJ3Dy0mH8oe9YD0TpOn+Y2RPh4eGQyWTQaDRQKpU4knjJ02ERERGREV5ZRURERERERC6TkpKEp5+eh5SUJE+HQkRm3KwoRO71k5BKpYiLi4NEImlQRiKRIC4uDoIgIDf/BG5WFHog0sZb9HYUlvzfNAwZNgQKhQJ+fn5QKBQYMmwIlvzfNCx6O8rTIRK5FD+DiciXtbgrqw4dOoQtW7YgKysLKpUKYWFhGD16NJYsWYJOnTo1KF9WVoZNmzbhq6++wq+//orQ0FCMGzcOf/7znxEYGOiBERARERGRt1Or1UhO3g0AiI6eDrlc3qhy7ojHUt/WYnI2Xn09rVYHQIRUKm3Q5549iVAqcxAePgBRUY8jNTXFaj+mdaZNm+Gy+auqUuHdd99CUVEh/vjHBzBjxhyrbd8ZnxaABFKp0GCezY29bgy7kJNzGhKJBBERAw3j0I8vKysTV69ehkIRgPHjx0Eul6OysqbBHOrjXrnyHwCAv/zlFezb9yWUyhz06xcOUQRyc3NQVFSIkJBQREYOhFwuR3T0dGg06nr1vvwyBYcPf4v27UMMbQuCAJ1Oi+LiYqhUKjz66DgIghQ//ngYoaFh6Nu3P/LylCgqKkRRUREAEQkJ2xAVFeOS14SIXEMURRw9exAiREyfPh29e/e2WLZPnz6YPn06du3ahaNnv8KEobPNLmx5o2f/ORWCTIBMkKFcU47kkmTkqHKg0qqgkCoQqYhEdEg0guRBWLwyGjqNDp/+da+nwyZqlKoqFbZs+RyiKOLf//4Mx479hP79I3D+/Fn06xcOQai7ZkGn0yEvT4m+fftDKpVBKhXw4IMP4//9v6UARIwdOwH+/gpER08HAOzatRPff/8tQkJC0a9fOM6eza2Xt2g0aqO86UHMmDHbJMfbhTNnsiCKIiIjB9XL8yZMmIjVq1dCFEX06xcBPz85Jk+OMuzX/2wtxzJuBwBeeeVv8PdXmJ0jfZ3aWjWUymyUlBQ3iNlWHqjPt0RRRJ8+/XD+/FmzuahxfMZjMpfbuir/tqdsY/N5fT0ALo3LVzSnsXijFrVY9cknn+C9996DIAgYNGgQQkJCoFQqkZiYiIMHD2Lbtm245557DOUrKiowb9485OXloWfPnnjwwQeRk5ODzZs34/Dhw0hISEBQUJAHR0RERERE3ig9fR927twKoO7ZH5MnRzeqnDvisdS3tZicjde4np5pn7t27QAAZGVl4vLlizh27Cer/ZjWCQwMcNn8rV37HrKzTwMA9uxJRHBwsNW2rY3P1r5du7YbtmdnnzaMw3h8AFBZWYlt27aZbcc47qysTADA3//+Kq5cuQwAhm16hYUFUCpzDG1kZ2eZrVdQ8JvFMRufsV1Q8BtycrIalKmp4XNuiLzNpcJc/Hb7Gtq2bYvFixfbLL948WJkZGTgt9tXcbkwFz07hjdBlI2z+J1oQAqUqEuw4cYGHLx1EDViTb0yB28dxMYbGzG23Vgs6bwEofJQLH4nGptWJHskZiJXWLv2PYiiaPhdqcwxfN6b5gKm25KTd6Oqqur3n/cAqMsRRBHYsycBQN3n/Zkz2YY6+rwlOzvLKG9KQHBwG5Mc706uk5OThStXLuLo0bo874cfDuPq1cuG9gDg4sXzhv0XLpw35IR65nIs43Y+/PA9vPzy38zOkbm8zDRmW3mgcb6lL2cuFzXuy3hM5nJbV+Xf9pR1RT6vf2+4Mi5f0ZzG4o1azG0Az58/jzVr1kChUGDHjh1ITEzEhg0bcODAAcyZMwc3b97EihUr6tVZs2YN8vLyMGPGDKSlpeHDDz9ERkYGoqKiDO0RERERERkrKyutd1CemLgdZWWlTpdzRzz5+dfM9m0tJmfjNa1nrs/ExPqLMMZfSpjrx1ydhIRtLpm//PxrDb4U2blzq8W2rY3PdJ5N95mOAagbh6V95trRx2Uat37ByZadO7c6Vc9e7nhPE5Fz1Npa/Hz+awBAbGws2rRpY7NOcHAwYmNjAQDHzn8NtbbWrTE21rP/nApIgUvVlzA/dz5Sb6Y2WKjSqxFrkHozFU/mPomL1RchkUrq6hP5IHP5iyP0C1XGEhK2YefOLVbr7dixxWLeZC5fA2BYtAFgWGCytN/cmMzlWMbtHD36E/LzrzWoZylnM47ZVh5obZ6Nc1HTvozHZJrbuir/tqesq/L5hIRt9V7bxsblK5rTWLxVi1msSkmpu2R04cKFGDp0qGG7XC7HihUr0L59e5w6dQr5+fkA6m7/95///AeBgYF4+eWXDZfKymQyvP766wgODsbu3buhUqk8Mh4iIiIi8k4JCdtQWVlp+L2yshKJiQ0PjO0t5454Vq16x2zf1mJyNl7Teub6tJZTm+vHXB2VSuWS+Vu16p0G26qqqiy2bW18pvNsus/cuFUqlcV95trRx2UubnuY+3LKldzxniYi55y+fASqmnKEh4dj6lT7F2WioqLQv39/qGrKkXXliBsjbDxBJqBEXYLYc7Eo0ZTYVadYU4zYc7EoVhdDkLWYr8momXE2D7BGpVKhutr6VdLm9uvzJls5nrOs5Vh65ubDUs4G3InZVh5obZ6Nc1FrfZnmtq7Kv+0p66p8XqVS1XttGxuXr2hOY/FWzeJTOCUlBXPmzMGwYcMwaNAgTJkyBZs2bar3D6ZcLke/fv0wcuTIBvXlcjm6du0KACgsrHto6M8//4zq6mr84Q9/aPBsqoCAANx3332orq7Gzz//7MaREREREZEvuXLlMjIy0hpsz8hIw9WrVxwu5654zPWRnr7PYkxHjvzoVLyW+jfuMz19n8X95vq5cuWyxTqNnb8jR360WH///tQG+2yNz1oszu4zlZGRhi+/THbp+8aV0tP3eW1sRC1JmeoWsq8eBQDExcVBKpXaXVcqlSIuLg4AkH31KMqrbrsjxEZb9HYUZIIMG25ssHuhSq9EU4KNNzZCJsiw6O0oN0VI5B7W8hdP2b8/1WqO1Fi2xnv16pV6VzPZytkA87me8b7U1BSb/WZkpOHoUfN5s2m5q1evWD0msNSOuXzXnmMLZ48/7Jm7xsTlK5rTWLyZTy9W6XQ6LF++HC+99BKys7MxdOhQjB49GoWFhXj//fcxZ84clJWVAQCef/557N27F/fdd1+DdlQqFc6fPw8A6NSpEwAYfu/Tp4/Zvnv16gUAyMvLc/m4iIiIiMj3iKKI+PhP6z0rQE+n0xn22VvOnfFYKm8ppg0b1jocrz392xubvh+dTofNmz+xWm7zZufmTz9Oa/71r02Gth2dX3fR6XTYsuVzj8ZgjSiK+Ne/PvH4PBG1dMfO/xc6UYtJkyZh0KBBDtcfPHgwJk6cCK1Oi2Pn/uuGCBtPJpOhXFOOg7cOOlX/wK0DqNBWQCZrUY93Jx9nT/7iKZ7+7N+wYQ10Op3LcjZrOaieTqfD+vXm82bTcps3f4rNm83nSI7k3/YcW1jL2RubzzcmLk+/R+zVnMbi7Xx6sWrbtm1ITU1Fjx49kJaWhs8//xzr16/Hf//7Xzz44IPIycnB66+/brOdTz/9FCqVCgMHDsRdd90FACgqKgIAhIWFma2j315S4tjZOkRERETUPB0/fgyZmSct7j916gSOH//Z7nLujscRFRXlFvdZiteV/ev72bVrO06fPmW1XGamc/OXmLjd6jgB4PTpU4a2XT2+xtBqtZ4OwarTp0+65D1NRM65XnIR14rPQ6FQYOnSpU638/zzz0OhUOBq8Tnkl1x0YYSN94eZPaGDDsklyRafUWVLjViD5OJk6KDDH2b2dHGERO5hT/7SUpWXl2PXrh0uy9nsXYyw9/XIzDxhNa8tL7cv/7bn2CIxcbtTxx+Ozp2jcflKfticxuLtfPp0kfj4eADAypUrDbfxA4DAwECsXr0aDz30EPbv34+4uDh07tzZbBuHDh3Cpk2bIAiC4bJ2AIb7bvr7+5ut17p163rliIiIiKjlUqvViI//zGY5e87IBID4+E8xePBQyOVyt8bjKqbxuqv/PXt22VVu8+ZPHJq/qioVkpLsa/tf/9qEyMgBTTq/zcE777zh6RCIWiStTouj574CADz77LMIDQ11uq3Q0FA888wz+PDDD3Hk3FeIbvc0pIL9txN0p/DwcEglUuSochrVTo4qB1KJFOHh4TiCSy6KrqGYmIlua5uI7tizJxFhYR08HYZbxMd/andOmpT0H7vac0U+70hcjT3maQr2zoMvjMUX+OyVVb/++ivy8/PRqVMnDB06tMH+oKAgjBkzBqIoWnyu1LfffoulS5dCq9XixRdfxKhRowz7BKFuaiQSidm6xpc0EhERERERERG1FOa/KfEc/a37VNrGnVCsr89bARIRETU9n12sKiwsBAB06dLFYhn91VbFxcUN9u3evRuxsbGoqalBbGwsFi1aVG+/QqEAAFRXV5ttu6am7rJyS1deEREREVHLIZfLsWDBMzbLLVy4CAsXPmuz3IIFzzbqrDx743EV03jd1f+0aTPsKrdw4SKH5s/fX4GYGPva/tOfFsPfX9Gk80tE5CypIMWoPo8CqHsEgrnvR+xVVFSEzz6rO7t8VJ9HveaqKgDQaDQAAIVU0ah29PX17RGRb5s2bSb+9KdFtgv6oAULnrU7J42JecKu9lyRzzsSV2OPeZqCvfPgC2PxBT57qoj+iiZLVz4Bd+7d7ufnV2/7mjVrsHHjRkgkErz66qtYsGBBg7odOtRdImopkbP1TCsiIiIialmGD78XgwcPtXg/8yFDhmH48JEAYHc5d8bjiMDAIIv337cUryv71/czY8Zc5OYqrd7ff/Bg5+Zv5sy52L8/1epzBgYNGmJo29XjawypVOrVz60aNGgoXn/9n1aP3YjsxdunOa5rSC/cHdob14rPY926dXjzzTedamfdunVQqVToFtoHXUJ6uTjKxlEqlRgybAgiFZE4eOug0+1EKiKhFbVQKpUujK6hpKQ0t7ZP7hMWFgQAKCryjudE6XQ6LFgwm8+tMiMoKAgzZsyBRCJxSc4mkUjsusOWtbzZ2ODBwyCKOot5bVBQkMXnVhnn3/Ycg8ycORdnz+Y6fPzhaL7raFyuOOZpCs1pLN7OZ6+s0i8mXbt2zWIZ/b6QkBAAdQtcr732GjZu3Ag/Pz+8//77ZheqAKBPnz4AgPPnz5vdf+HCBQBAv379nIqfiIiIiJoXiUSChQsXmf1CXhAELFjwLCQSid3l3BmPpfKWYoqNfcHheO3p397Y9P0IgoA//Wmx1XILFzo3f/pxWvOnPy02tO3o/LqLIAh46invvcpLIpHgT3/y/DwRtXT39n4EUkGKffv2ITMz0+H6mZmZSEtLg1SQ4t4+j7ghwsY5kngJAgREh0SjlaSVU220krRCdGg0BAg4kui+51URuZI9+YunePqzf8mSFyAIgstytoULbV+hZS1vNi23cOGz9XJL0/1LltiXf9tzbFHXn+PHH47MnTNxefo9Yq/mNBZv57OLVZ07d0aXLl1QUFCAkycbrmqWl5fjhx9+gCAIGDFiBABg5cqV2L17NwIDA/H5559j4kTLZ2SNHDkSrVu3xk8//QSVqv49jysrK/HTTz9BoVBg+PDhrh0YEREREfmsbt26Y/z4hjnm+PET0a1bd4fLuSsec31MmDDJYkyjRt3nVLyW+jfuc8KESRb3m+unW7fuFus0dv5GjbrPYv3HHpvcYJ+t8dmaG2f2mRo/fiImT45y6fvGlSZMmOS1sRG1JG0U7RB5d91zulevXu3Q1ZharRarVq0CAAzoNgpB/m3dEWKjaTQaBMmCMLbdWKfqj2s3DoHSQN4CkHyOtfzFUx57bLLVHKmxbI23W7fuGDXqvnq/24rHXK5nvM+efMta3mxarlu37laPCRzJv+05tnD2+MOeuWtMXL6iOY3Fm/nsYhUAPPXUUwCAV155BdevXzdsr6ysRFxcHCoqKjBu3Dh06NAB3333HeLj4yGTybBp0ybce++9VttWKBSIjo5GaWkp3nzzTUOyotFo8NZbb6GsrAwzZ85EYGCg+wZIRERERD5n1qx5CAgIMPweEBCAmTPnOl3OHfHExa0w27e1mJyN17SeuT71z4s1x1w/5uooFAqXzF9c3IoG2/z9/S22bW18pvNsus/cuBUKhcV95trRx2Uubnu4+xm87nhPE5FzBvX4AxStgqBUKrF3716766WkpCA3NxcBrdpgYPc/uDHCxvnktRRodBos6bwEIbIQh+qGykLxXOfnoNFp8MlrKW6KkMh9nM0DrFEoFGjdurXVMub26/MmWzmes6zlWHrm5sNSzgbcidlWHmhtno1zUWt9mea2rsq/7SnrqnxeoVDUe20bG5evaE5j8VY+vVg1f/58PPbYY7h8+TImTpyIp59+Gn/+85/xyCOP4JtvvkFERATeeOMNAMBHH30EoO6WgAkJCfjLX/5i9j/97f0A4MUXX0TPnj2RnJyMCRMm4Pnnn8eECROQnJyMiIgILF261BPDJiIiIiIv1qZNMGbMuHPQMnPmXLRpE+x0OXfE06XL3Wb7thaTs/Ga1jPX58yZ8+rtu/fe+xqUM23TtM6sWfNcMn9dutxdr38AmD17vsW2rY3PdJ5N95mOAagbh6V95trRx2Uad/fuPWzWB+rG5kw9e7njPU1EzpFL/TCy98MAgPXr16OsrMxmndLSUqxfvx4AMLL3Q5BL/WzU8CydRodQeSjW91lv94JVqOz38vIQaNXe+/w/ImvM5S+OMHfyyqxZ8zB79pNW682Z86TFvMlcvgbA5IqnHlb3mxuTuRzLuJ1Ro+5Dly53N6hnKWczjtlWHmhtno1zUdO+jMdkmtu6Kv+2p6yr8vlZs+bVe20bG5evaE5j8VYyZyv+/PPPLglg5EjnHz4mCAI++OADjBkzBrt27cKJEycAAD169MCiRYswb948+Pn5oaqqCllZWQCAgoICfPnllxbbfOKJJ3DPPfcAANq2bYuEhAR89NFH+Oqrr/DNN9/grrvuwjPPPIP//d//tbqCT0REREQt14QJk1BdXfX7z5MbXc4d8Vjq21pMzsarr6fV6iCR1OXxpn1WVlZAqcxBRMQATJ0ag9TUZKv9mNZx5fwtW7Yc7777FoqKCjFmzEM2274zPi0kEkm98Vkbe90YKpGTcxoSiQSRkQNN9lUgKysTV69ehkIRgMcemwCpVAqVqrbBHOrjXrnyHwDqzvxNTU2BUpmD/v0jIIqAUpmNoqJChIaGITJyIGQyGSZMmIxHHhlXr97evV/g8OFvERISColEAp1OB0EQoNNpUVxcDJVKhbFjJ0AQJPjhh8MIDQ1Dv34RyM3NQVFRIYqKCl32WhCRa/Xs0B95+Sfw2+1r2LRpE+Li4qyW37RpE0pLS9GpbTf06NC/iaJ03qd/3YvF70SjV+te2Np/Kzbe2IgDtw6gRqxpULaVpBXGtRuH5zo/hxB5CKAFPvub5e+LiLzdsmXLMXfudMPvEREDEB4egbNn89CvXwSkUgGiKEKnE5GXdwb9+oVDKpVCEAQ8+ODD+H//bykAEePGTUTr1q0xYcJkiKKIsrIyfP/9t79/3ocjL09ZL2955JFxFvMmfa5z5kzd97KRkQMRFRWDL79MNuxfvfpdiKKI8PBIyGQyTJkSbdg/eXI0UlOTreZYxu3UzYPlf9f0dWpr1cjNzUFxcVGDmG3lgfp8SxRF9OvXH2fP5pnNRY3jMx6TubzSVfm3PWUbm8/r64mi6NK4fEVzGos3koiiKDpTsX///o1+cJhEIsGZM2ca1UZzVVurQWlplafDcKmwsCAAQFFRuYcjISJn8G+YyPfx75jIt/nK3/CcOdNQXV2F1q39sWPHHk+HQ81MTMyd50XMum+ZByPxXTcrCrH32GYIUgHbt29H7969zZY7d+4c5s6dC1EUMXXkQrQP7NDEkTrv2X9OhSATIBNkqNBWILk4GTmqHKi0KiikCkQqIhEdGl33jCqdBlq11u0LVQk/rTX8nJSU5ta+yH28/bOYn8FE1nn733Bzop9rRzh9ZVVwcLDZxSq1Wo3KykoAdfct7dGjB4KCglBTU4PLly+jrKwMEokEvXr14vOeiIiIiIiImpmZM+di794kTJ0a4+lQiMiM9oEd0L/rUCivn8CqVavw8ccfN/h+RxRFrFq1CjqdDuFdh/vUQhVQd4UVACx6OwoBsgDM7jAbUonUsF8raiFAgFqt5jOqqFnhZzAR+TKnF6uOHj3aYFtFRQXmzZuH/Px8vPrqq5g8eTL8/O7cz1gURRw8eBBvvvkmNBoNNm7c6Gz3RERERERE5IWiomIQFcUvyYi82dCeo3GxQInjx4/jq6++wtixY+vtP3jwIE6cOIFWcn8M7TnaQ1E2nn4h6g8zeyI8PBwymQwajQZKpRJHEi95ODoi1+NnMBH5MqcXq8z58MMPkZeXh82bN+MPf/hDg/0SiQTjxo1Dhw4dMGvWLKxatQrvvvuuK0MgIiIiIiIiIiIrWsn9MbzXA/gxLx1r1qzBH//4R/j7+wMAqqqqsHZt3S3rhvd6AK3krT0ZqkscSbyEI+DiFBERkTcTXNlYeno6unfvbnahytiQIUPQu3dvHDp0yJXdExERERERERGRHfp0HoSQoI4oKChAfHy8YfvmzZtRUFCAkKBO6NN5kOcCJCIiohbFpYtV5eXlaN3a/jNuqqurXdk9ERERERERERHZQZAIGNW37vZ/W7duxfXr13H9+nVs3boVAPCHvmMhSFz6tRERERGRRS69DWDXrl1x7tw5XLhwAffcc4/FcidPnsT58+cxcOBAV3ZPRERERERERER26hjcFVKpFLW1tfjggw8giiLUajXu6TQAHYK7eDo8IiIiakFculg1bdo0rFy5EosXL8bKlSsxYsSIBmW+/vpr/PWvf4VEIsHs2bNd2T0RERERERERtRAJP631dAjNglwuh1arrfeohvzb5zm/RERE1KRculg1Z84cfP311zh27Bjmz5+PTp06oVevXlAoFFCpVMjLy0NJSQlEUcSkSZMQExPjyu6JiIiIiIiIiMgBEokEMpkMGo0GACCTySCRSDwcFREREbU0Ll2s8vPzw6ZNm7B+/Xps374dv/76K3799dd6ZYKDg/Hcc8/hySefdGXXRERERERERETkBJlMBq1Wa/iZiIiIqKlJRFEU3dFwbW0tfv75Z1y+fBkVFRVo06YNevbsiREjRjDxsUNtrQalpVWeDsOlwsKCAABFReUejoSInMG/YSLfx79jIt/Gv2Ei3+btf8O//HIMADBixL0ejoTIe3n73zERWce/4aajn2tHuG3VyM/PD/fffz/uv/9+d3VBREREREREREQuwEUqIiIi8iSnF6uqququ+vH392+wzRHG9YmIiIiIiIiIiIiIiKhlcXqxaujQoRAEAfv27UPPnj0BAMOGDXOoDYlEgjNnzjgbAhERERE1Q7wNEREREREREVHL0qjbAOp0unq/O/r4Kzc9LouIiIiIfJRarcbHH38EANi48XPI5XIPR0RERERERERE7ub0YtV///tfAEDHjh0bbCMiIiJqzmJiJno6hGZLrVZDo9EAqJtnLla5XlJSmqdDICIiIiIiIqrH6cWqLl26WN0miiJycnJw+fJlVFZWQqFQoHv37oiMjIRUKnW2WyIiIiJqpkRRNCxUAYBGo4FMJoNEIvFgVERERERERETkbo26DaAle/bswYcffojCwsIG+9q2bYtly5Zh1qxZ7uiaiIiIiHyUWq0GADzwwAMQRRHfffcd1Go1/Pz8PBwZEREREREREbmTyxerVq1ahX/9618QRRF+fn7o1asXFAoFysvLcenSJdy6dQtvvvkmrly5gpdfftnV3RMRERE1qXF9FZ4OoVm4WanG4fNV8PPzw4svvggAOHLkCGprazGiixztA3g7wMY4cFbl6RCIiIiIiIiILHLpYtVPP/2Ezz//HH5+fli+fDlmzpyJ1q1bG/ZXVVUhMTER77//PuLj4/HQQw/h3nvvdWUIRERERORjRFHE6fxKAMD8+fPRtWtXAMC8efPwr3/9C6fzK/FAn2DeDpCIiIiIiIiomRJc2diWLVsgkUjwj3/8A0899VS9hSoA8Pf3x4IFC/DWW29BFEXs2LHDld0TERERkQ+6crMGpVVadOzYEQsWLDBsX7hwITp27IjSKi2u3KzxXIBERERERERE5FYuXaw6deoUwsLCEBUVZbVcdHQ0wsLCcOrUKVd2T0REREQ+plajg/LXulvUvfDCC/D39zfs8/f3x7JlywAAyl9VqNXoPBIjEREREREREbmXS28DWF5ejoiICLvK3nXXXVAqla7snoiIiIicNHHJOwgPD4dMJoNGo4FSqUTahhVu7zf3tyrUakUMHz4cjz76aIP9Y8eOxe7du3HixAnkFlRhUJcAt8dERERERERERE3LpVdWtW/fHleuXIFOZ/2sV61WiytXrqBdu3au7J6IiJq5lJQkPP30PKSkJHk6FKJm45WPU/HXz9IxZNgQKBQK+Pn5QaFQYMiwIfjrZ+l45eNUt/VdWqXB5ZJqSAUBcXFxZp9JJZFIEBcXB0EQcLm4GmVVGrfFQ0RNg5/nRERERERkyqVXVo0cORJpaWn4/PPP8eyzz1os9/nnn6O0tBSTJk1yZfdEzYZarUZy8m4AQHT0dMjlco/GodXqAIiQSqUui8cdY7TWpjP9VVWp8O67b6GwsAChoWEYMGAwpk2b4VCslvrVb1er1VAqcyAIAl555W/w91dYbWPy5CikpqagpqYahw8fgkpViW7duiMiYiAkEgny8pTo27c/pFIZpFLB4jxotVoAEmi1WuTkZOLChfOQy/0wYcJETJs2EykpScjKykRJSTFCQ8PQt28/HDv2E8LCwtC7d3/4+flh8uQopKR8AaUyG3379gcgwdmzSoSHD6g3T1VVKqxc+Q8AwCuv/A0ymRx79iQiJycLEokEffv2w9mzeQCAPn364vz5c4Y2NBo13n33LRQVFWLEiFHYty8FALB9+78RFRVj9+tARA29vPFLCDIBMkGGck05kkuSkaPKgUqrgkKqQKQiEtEh0QiSB+HVT/dBp9Hh/56b4rL+RVFEVn4lRADTn3gCvXv3tli2T58+mD59Onbt2oWs/Er8zz1tzC5sEZH3U6vV2L49HhqNBv/+92fYuvVfkEqlGD9+Ii5dulgvJ1Kr1fjPf3bi8OFv0b59CMLDI5CXlwudTouSkhKEhoYCkEAQBPTu3Qc//vg9dDodJBIJOnbshLi4V5GengatVgudToe8PCXCwyMxbdrMBjkZYD5HNJeHmcuNTfPmOvp/p0TodDoolTmQSCTo1y8CUqnUSht1eZpUKmDChIlYvXolAOAvf3kF6elpDWI1F6Ol8ejL79mTCKUyp0HeZmnc+pzP2vzZ07elPhwpb+nYxJ3HUN5yfEZERETUnLl0serpp59Geno6PvjgA/z666+YPXs2+vTpY9h/9uxZ7Ny5E4mJiZBKpVi4cKEru7fLoUOHsGXLFmRlZUGlUiEsLAyjR4/GkiVL0KlTpwblH330UVy7ds1iezk5OZDJXDqNREhP34edO7cCqHtex+TJ0R6PQ89V8bhjjNbadKa/tWvfQ3b2aQBAYWEBzpzJRmBggEOxWurX3Nx++OF7ePnlv1lt4+LF8zh69Kd6+5XKM1Aqzxh+z8rKNPxsbR5MqdVqJCX9B9evX8exY3f60I8dAG7cuIHMzMwGsRj3mZWVWW+e1q59z7D/ww/fQ0TEQOzatcNQXj/Hxj/r28jOzjJs0y9UAYBGozY7BiKyz6ub9gFSoERdgg03NuDgrYOoEWvqlTl46yA23tiIse3GYknnJQiVh+LVTfvw7mLXnGx043YtSio1aNu2LRYvXmyz/OLFi5GRkYHi0lLcKK1Fl7atXBIHETWt9PR90GjuXCGp0+mg0+kMixzAnZwoPX0fdu9OAAAUFPwGpTKnXluFhQWGn43zCQAoKirE3//+Kq5cuVxve12OEWg2JzOXIxrvv3DhfL0cybiOtRzLlGms1tr44YfDuHq1bgx/+9urhp8t5ZXG+ZmlnDc9fZ8hFzPN22yN29r8GZezJ992ND+3dWzizmMobzk+IyIiImrOXHobwIiICKxYsQKiKGLnzp2YOnUqBg4ciFGjRmHgwIGIiopCQkLdwcaKFSswYMAAV3Zv0yeffIJFixbhxx9/RM+ePTFmzBgAQGJiIh5//HFcuHChXvny8nJcv34doaGhmDJlitn/BMGlU0iEsrJS7Nq13fB7YuJ2lJWVejwOV8bjjjFaa9OZ/vLzrzX4MgIAEhK22R2rpX4tze3Roz8hP7/+4rhpWdOFKluszYMl5sZtjrVY9PNkOo9Hj/6EnTu32NX+jh1brcZy4sTPdrVDRPW9vPFLQApcqr6E+bnzkXoztcFClV6NWIPUm6l4MvdJXKy+CIlUUle/kTRaEdm/qgAAsbGxaNOmjc06wcHBiI2NBQBk31BBoxVt1CAib2NvLnL06E/Iy1MiIcG+xR9LTBeq9PR5iq0c0XS/ubwkMXE78vOv2TUuS6y1oV+cMv3ZUl5pnJ+Zy3nLykqRmLit3jbT/NbWuC3Nn3E5W/m2o/m5rWMTdx5DecvxGREREVFz5/KVlrlz5yI+Ph733nsvpFIp1Go1SktLoVarIZVKMWrUKMTHx2Pu3Lmu7tqq8+fPY82aNVAoFNixYwcSExOxYcMGHDhwAHPmzMHNmzexYkX9h4grlUqIooiHHnoIq1evNvsfF6vI1RIStqGystLwe2VlJRITnT/4dVUcrozHHWO01qYz/a1a9Y7Z7SqVyu5YLfVraW7N9WutrD2szYM76efJ3DxWV1fb1UZ1dZXV/f/3f/90Kjailk6QCShRlyD2XCxKNCV21SnWFCP2XCyK1cUQZI3Pfc4VVqFarUN4eDimTp1qd72oqCj0798f1WodzhVa/zeCiLyPI7nI22+/jqoq9/yd6/MUWzmiPfFWVlZi1ap3Gp2vOdqGPXmluZw3IWEbVCpVvW2m+a2tcVuaP1t9m8bhSH5u69jEncdQ3nJ8RkRERNTcuWWlZdSoUfj3v/+NY8eOISUlBTt27EBycjKOHTtmWMhqaikpKdBqtVi4cCGGDh1q2C6Xy7FixQq0b98ep06dQn5+vmHfmTN1t9WKjIxs8nipZbpy5TIyMtIabM/ISMPVq1c8Hocr4nHHGK21eeTIjw73d+TIj1ZjsSdWSzGlp++zOrdXr14xnBFr63Wwl7V5cKf9+1Pd+r5Vq9XYunWz29onao5e+TgVMkGGDTc22L1QpVeiKcHGGxshE2R45eNUp2OorNHifFHdF9BxcXG/P7fFPlKpFHFxcQCA80VVqKzROh0HETUtR/OaiooKN0ZTl6dYyxEdidcV+Y4zbWRkpCE9fZ/NMvq2r1y5bLG8o+O2NH+W+jbm6PGArZgs5deuOIbyluMzIiIiopbArZcFKRQK9OvXD8OGDUP//v2hUCjc0k9KSgrmzJmDYcOGYdCgQZgyZQo2bdpU7+x9uVyOfv36YeTIkQ3qy+VydO3aFQBQWFho2K5UKgFwsYqahiiKiI//FKLY8LZGOp3O4r6mjKOx8bhjjLba3LBhrUP96etYo9PpsHmz5VitxSSKos0xbtiwBlqt1mWvubV58HVffPGfes+9ICLrZDIZyjXlOHjroFP1D9w6gAptRaOe15l9oxI6EZg0aRIGDRrkcP3Bgwdj4sSJ0Il1bRGR97Mnv/QESzni5s2feGW8pnQ6nV1l4uM/NYzLWrl//cuxcdsqZy7fdvR4wJ73jqX8urHHUN5yfEZERETUUvj0Pex0Oh2WL1+Ol156CdnZ2Rg6dChGjx6NwsJCvP/++5gzZw7KysoAAM8//zz27t2L++67r0E7KpUK58+fBwB06tTJsP3MmTOQSqW4dOkS5s6dixEjRmDkyJH43//9X5w+fbpBO0SNcfz4MWRmnrS4/9SpEzh+3P3P6LEVR2PicccYbbVZUVHuUH+Jidut1tHLzLQcq71zaEl5eTnWrl3VqDZM2TMmX/XGGytsFyIiTFzyDnTQIbkk2eIzqmypEWuQXJwMHXSYuMT87VKtKSirxW9laigUCixdutSpGIC6vE6hUOC3MjUKy2qdboeImkZjc6Omlpl50qfiteXUqRPYtWs7Tp8+ZbXc6dOuH7dpvu3o8UBj3zuNOYbyluMzIiIiopbC+dNivcC2bduQmpqKHj164PPPPzdcHVVRUYHly5fj22+/xeuvv44PPvjAajuffvopVCoVBg4ciLvuugsAUFtbi4sXL0Kr1eKll17CwIEDMWrUKJw7dw7ffPMNvv/+e6xevRoTJkxw+zip+VOr1YiP/8xmufj4TzF48FDI5XKPxuFMPO4Yo6Px2uqvqkqFpKRddtfdvPmTBrG6IiYA+P777xrdRktx5kw2YmImejoMIq8XHh4OqUSKHFVOo9rJUeVAKpEiPDwcjtxcVKcTkfX7lVDPPvssQkNDnY4hNDQUzzzzDD788EOcvlGJhwPlEASJ0+21NPw3k6jl2bPH/hzX1fT5dt3P9h8P2Fve3vYcOYbyluMzIiIiopbEp6+sio+PBwCsXLnSsFAFAIGBgVi9ejWCgoKwf/9+3Lhxw2Ibhw4dwqZNmyAIguEZCACQl5cHjUaDgIAAxMfHY/fu3Vi/fj0OHDiAV199FWq1Gq+++iqKiorcNj4iIiIiV9Hfuk+lVTWqHX39xtwK0JW4REVERERERETk+3x2serXX39Ffn4+OnXqhKFDhzbYHxQUhDFjxkAURfz8s/lL87/99lssXboUWq0WL774IkaNGmXYN3DgQHz//ff48ssvG9w6cMGCBXj00UehUqnwxRdfuHZg1CLJ5XIsWPCMzXILFjzr1rP27I3DmXjcMUZH47XVn7+/AjExM+yuu3DhogaxuiImAPjjH8c0ug0iImP657sppI17hqi+vqPPixMECQZ2DgBQd1V7cXGx0zEUFRXhs8/qzngf2DmAV1UREdkwbZr9Oa6r6fNtR48HXJVXO3MM5S3HZ0REREQtiXecEuuEwsJCAECXLl0sltFfbWXuy5Ddu3fj9ddfh0ajQWxsLBYtWtSgTFhYmMW2H3roIXz11VfIzs52NHQis4YPvxeDBw+1eF/0IUOGYfjwkR6PozHxuGOMttoMDAyy+Lwmc/3NnDkX+/en2nzG0+DBlmO1dw4tCQoKwrJlcSgvL3fZcwOszYOv27Vrr9dc4UEthy/eRk2pVGLIsCGIVETi4K2DTrcTqYiEVtRCqVQ6XLdjGz90aiPHb2UqrFu3Dm+++aZTMaxbtw4qlQqd2sjRoY2fU220ZElJjtzAkbxJWFgQAKCoyLc+00VRxFtv/dVnngOlvwWdr8Rry5AhwzBjxlzk5iqtPrdq0KChkEhcO27TfNvR44HG5tWNOYbyluMzIiIiopbCZ6+sEkURACCRWD6TVqvVAgD8/Op/ibFmzRq89tpr0Gq1ePXVV/H888873L9+Iau6utrhukTmSCQSLFy4yOx7WhAELFjwrNX3e1PE0dh43DFGW23Gxr7gUH/6OtYIgoCFCy3Hai0miURic4xLlrwAqVRq83Wwl7V58HWPP/4EF6qI7JS2YQUECIgOiUYrSSun2mglaYXo0GgIEJC2YYVTbQzoHABBAuzbtw+ZmZkO18/MzERaWhoESV1bROT97MkvPcFSjrhw4SKvjNeUINg+nNfnvIIg4E9/Wmy13J/+5Ni4bZUzl287ejxgz3vHUn7d2GMobzk+IyIiImopfHaxqkOHDgCAa9euWSyj3xcSEgKgboHrtddew8aNG+Hn54f3338fCxYsMFs3LS0Ny5cvx5dffml2//Xr1wEAnTp1cnYIRA1069Yd48c3PFt//PiJ6Natu8fjcEU87hijtTZHjbrP4f5GjbrPaiz2xGoppgkTJlmd227dumPUqPustuEoa/PgTo89Ntmt71u5XI758xe6rX2i5kij0SBIFoSx7cY6VX9cu3EIlAY6fAtAYwGtpOgd5g8AWL16teHkIntotVqsWrUKANA7zB8BraROx0FETcvRvCYwMNCN0dTlKdZyREfidUW+40wb48dPxIQJk2yW0bfdrVt3i+UdHbel+bPUtzFHjwdsxWQpv3bFMZS3HJ8RERERtQQ+u1jVuXNndOnSBQUFBTh5suFl+eXl5fjhhx8gCAJGjBgBAFi5ciV2796NwMBAfP7555g40XLCW1JSgtTUVOzcubPBPlEUsXfvXgDAH//4RxeNiKjOrFnzEBBw50zxgIAAzJw51+NxuDIed4zRWpvO9BcXZ/6KAYVCYXeslvq1NLfm+rVW1h7W5sGd9PNkbh5bt25tVxutW/tb3f/yy391Kjailmzl/06GRqfBks5LECILcahuqCwUz3V+DhqdBiv/d3Kj4ujTwR+t5QKUSqUhp7JHSkoKcnNz4S8X0KeD9X8jiMj7OJKLvPbam/D3d8/fuT5PsZUj2hNvQEAA4uJWNDpfc7QNe/JKcznvrFnzoFDUf3ahaX5ra9yW5s9W36ZxOJKf2zo2cecxlLccnxERERE1dz67WAUATz31FADglVdeMVzpBACVlZWIi4tDRUUFxo0bhw4dOuC7775DfHw8ZDIZNm3ahHvvvddq25MmTUJgYCCOHz+O+Ph4w3ZRFLF+/XqcOnUKffv2xcMPP+yWsVHL1aZNMGbMuHPwM3PmXLRpE+zxOFwZjzvGaK1NZ/rr0uVu3HvvfQ22z5o1z+5YLfVraW5HjboPXbrcbbUN/VVX9rI2D5aYG7c51mLRz5PpPI4adR9mz37SrvbnzJlvNZZhw/iMACJn6DQ6hMpDsb7PersXrEJlv5eXh0Crtv9KKEtkUgkG3FX3Zen69etRVlZms05paSnWr18PAIjsrIBMylsvEfkae3ORUaPuQ79+4Zg1a36j+uvevYfZ7fo8xVaOaLrfXF4yc+ZcdOlyt13jssRaG9269TD7s6W80jg/M5fztmkTjJkz59XbZprf2hq3pfkzLmcr33Y0P7d1bOLOYyhvOT4jIiIiau58+kEf8+fPx8mTJ7F//35MnDgRI0eOhL+/P3755RfcunULEREReOONNwAAH330EYC6WwImJCQgISHBbJvPPfcc7rnnHrRv3x7vvPMOli9fjnfffRe7d+9Gr169kJeXh8uXLyMsLAzr1q3js1LILSZMmITq6qrff27c2euuiEOr1UEiqbs3u6vicccYrbXpTH/Lli3Hu+++hcLCAoSFdcCAAYMcjtVSv/rtarUaublnIJFIsGxZnM02pkyJxpdfJqOmphqHDx+CSlWJ7t17IiJiACQSCXJzz6Bfv3BIpVKzr9ed11QLiUQCjUaLnJzTuHDhHORyPzz22GTExMxASsoeZGVloqSkGKGhYejbtz+OHfsRHTp0QJ8+4ZDJZJgyJRrJyUlQKrPRr184ACAvT4mIiAH1+l22bDlWrvzH7z/HQSqVorKyAjk5WZBIJOjXrz/y8nIBAP369cfZs3mGNh55ZBzeffctFBUVYsSIUdi3L8Wh+Seihv7vuSl4ddM+9GrdC1v7b8XGGxtx4NYB1Ig1Dcq2krTCuHbj8Fzn5xAiDwG0wP+3ZKpL4ujc1g8hJTKU3L6NTZs2IS7O/L+Beps2bUJpaSlCA2ToHOxntSwRea8JEyZh8+ZPDL8LggCpVIoJEybh0qWL9XKiCRMmoby8DIcPf4uQkFCEh0cgN1cJnU6LkpIShIaGGZ5X1KdPX/zww2HodDoIgoAOHTripZdew/79qdBqtdDpROTlnWmQp9jKEY33T54cjdTUZLO5sWneLIoiJBIJRBGQSOpuY5qbewYAEB4eCYlEsNJGXZ5Wt28SVq9+F0DdFfj796c2iNVcrmhpPPrylZUVUCpzGsyHpXGnpNTlfNbmTz8/1vq21Icj5S0dm7jzGMpbjs+IiIiImjOJKIqip4NoDFEU8cUXX2DXrl3Iy8sDAPTo0QNTpkzBvHnz4Ofnh6qqKgwbNgw6nc5me1u2bMGoUaMMv2dnZ2PTpk34+eefUVFRgQ4dOuDhhx/GkiVL0L59e7eNq7ZWg9LSKre17wlhYUEAgKKicg9HQkTO8Ia/4TlzpqG6ugqtW/tjx449HouDKCbmzq2Ex/VVWCnpvV7e+CUEmQCZIEOFtgLJxcnIUeVApVVBIVUgUhGJ6NDoumdU6TTQqrUuW6jSK63S4NDZUgiCgO07dqB3795my507dw5z586FqNPhwb7BaOPPk4UcdeCsyvBzUlKaByOhxvCGz2JX4Oc5tVTN5W+YqCXj3zGRb+PfcNPRz7UjfP5IXyKRICYmBjExMRbL+Pv7Q6lUOtX+gAEDsG7dOmfDIyIiF5o5cy727k3C1KmW/80nIvv833NTAACvfJyKAFkAZneYDalEativFbUQIECtVjf6GVWWBPvL0COkNS6VVGPVqlX4+OOPIZHUv72fKIpYtWoVdDodeoa25kIVUTPAz3MiIiIiIjLFo30iIvIZUVExiIriF1tErqRfiJq45B2Eh9fd5lOj0UCpVCJtwwq399+/kz/yb9fg+PHj+OqrrzB27Nh6+w8ePIgTJ07ATypB/47+bo+HiNyPn+dERERERGSKi1VEREREhLQNK+CJm8P5yQSE36VA5vVKrFmzBn/84x/h71+3KFVVVYW1a9cCAMLvUsBPJnggQiIiIiIiIiJyNx7xExEREZFHdW/fCsH+UhQUFCA+Pt6wffPmzSgoKECwvxTd27fyXIBERERERERE5FZcrCIiIiIij5JIJBjUJQAAsHXrVly/fh3Xr1/H1q1bAQCDugQ0eJYVERERERERETUfvA0gEREREXlc+wA5urbzw/Vbtfjggw8giiLUajWkUinaB8g9HR4RERERERERuREXq4iIiIga4cBZladDaDZEse6i/0OHDhm2yeVyzjERERERERFRM8fbABIRERGRV5BIJJDJ7pxLJZPJePs/IiIiIiIiohaAi1VERERE5DX0C1SmC1dERERERERE1HzxGwAiIiIiByUlpXk6BKeEhQUBAIqKyj0ciXW//HIMADBixL0ejoSIiIiIiIiImgIXq4iIiIjIq3CRioiIiIiIiKhl4W0AiYiIiIiIiIiIiIiIyGO4WEVEREREREREREREREQew8UqIiIiIiIicotffjlmeA4dERERERGRJXxmFRERERERtRgxMRM9HUKLIYoiampqAACtWrWCRCLxcEQtR1JSmqdDICIiIiJyCK+sIiIiIiIiIpfTaDQQRRGiKEKj0Xg6HCIiIiIi8mJcrCIiIiIiIiKXMl2g0i9cERERERERmcPbABIRERERUYv00dTXPR1Cs/XZkUT8dOUkHnjgAYiiiO+++w5qtRqfTH/b06E1W3/e+6anQyAiIiIichoXq4iIiIiIiMhlzhdfwU9XTsLPzw8vvvgiAODIkSOora3F+eIr6B3a3cMREhERERGRt+FtAImIiIiIiMgldDoddpzYCwCYP38+unbtiq5du2LevHkAgB0n9kKn03kyRCIiIiIi8kJcrCIiIiIiIiKXOHzpZ1y5lY+OHTtiwYIFhu0LFy5Ex44dceVWPg5f+sVzARIRERERkVfiYhURERERERE1WkWNCkmnMwAAL7zwAvz9/Q37/P39sWzZMgBAUlYGKmtVHomRiIiIiIi8ExeriIiIiMirpKQk4emn5yElJcnToVATCwiQIji4FQICpJ4OhZyQkn0QFbUqDB8+HI8++miD/WPHjsWwYcNQUVOJlOyvPBAhETUVfpYTERGRo7hYRUREREReJTFxO27duonExO2eDoWaQNu2rREaGoj2oQooFAr4+flBoVCgfagCoaGBaNu2tadDJDtcu/0rvrlwBFKpFHFxcZBIJA3KSCQSxMXFQRAEfHP+CK7f/s0DkRJRU+BnORERETlK5ukAmtqhQ4ewZcsWZGVlQaVSISwsDKNHj8aSJUvQqVOnBuVFUURSUhJ27dqFs2fPQqfToX///njqqacwceJED4yAiIjIs9RqNZKTdwMAoqOnQy6X211Ov622Vo28vDOQSCR45ZW/QSaTNyhbVaXCypX/AAC88srf4O+vMLS7Z08ilMochIcPwLRpMyzG4I6xmcalj12r1QEQIZVKzdY1bRuAXfVM606eHIXU1BRotVrodDrk5p6BVquFRCKBIAiIiBhYb07UajV27dqBw4e/hVQq4JFHHkFVlRp5eUqIoggA6N27D3788Xvcvn0b99xzDwYMGAy5XI7Jk6OQkpJUb641GjVWrvwHRFFEnz59cf78OYSHD0BU1OOGsn369EVurhIlJcX44x8fxIwZsw2v/549u5CVdQolJcUIDQ1D//7hOHfuLMLDIxEVFYM9exJRXV0FAIb/U/MUHNwKgkyATJChXFOO5JJk5KhyoNKqoJAqEKmIRHRINILkQWgXIoFOo0NpaY2nwyYzRFHEzhN7IYoipk+fjt69e1ss26dPH0yfPh27du3CjpN7Effgs2YXtojId1VVqep9lickbAMAaDRqfP/9dxBFEWFhHRARMQCCIECr1eHMmSxcvXoF3bp1Q2TkIOh0Whw+fAiVlZXo1q0bZDI5IiIGYvz4CVixIg6iKGLUqPtw7NgRhIV1wKuv/h0A8M47b6K4uAijRz+IJ56YDQBm81F9LtmvXzgEQX8etwRSqWCUa+mgVtfihx++M+RMHTt2wquv/t2Ql+rHa5qz6nM3rVbboF3jWBzVmHzStL5pPKZzZW7ubLGUvzvL3uMOZ8u7iqV+PRUPEZGvalGLVZ988gnee+89CIKAQYMGISQkBEqlEomJiTh48CC2bduGe+65x1BeFEW89NJL2Lt3LxQKBUaNGoXKykocP34cL774IkpKSjB//nwPjoiIiKjppafvw86dWwHUPYNk8uRou8sZb9P78MP3EBExsEHZtWvfQ1ZWpqHMyy//zdDurl07AABZWZkIDAywGIM7xmYal3HseubqmrYtirCrnmndCxfO49ixnyyOITv7dL05SU/fhz17Eg37t23b1qBOTk6W4efcXCVyc5UN+tLPdXZ2lmH82dmnDfsuX75Yr6zenj0JCA5uY3j9d+26c4Z1YWEBzpzJNtS5cuUSjh61PDZqPtq18wekQIm6BBtubMDBWwdRI9ZfiDp46yA23tiIse3GYknnJQiVh6JdO3/cusVFTG/z87XTyCu6hLZt22Lx4sU2yy9evBgZGRnIK7yIX65lYWS3QU0QJRE1lbVr36v3uz5vM1ZUVGjIAYwplWegVJ6pt02fl2Rnn8aBA/tx+/ZNAMCXXyYDAAoKfsOHH74HUbyT0+zenYA2bdrUy7eM81HjXNLUxYvnLeYjRUWF9fJS/XhNc1ZzOa9xXmUth7amMfmkaX3jcZq2Ze53e+K1lL87y97jDmfLu4qlfj0VDxGRr2oxtwE8f/481qxZA4VCgR07diAxMREbNmzAgQMHMGfOHNy8eRMrVqyoVycpKQl79+5F3759kZGRgY8//hhbt27Fli1b4Ofnh5UrV6KkpMRDIyIiImp6ZWWl9RYbEhO3o6ys1K5y+fnX6m3TO3r0JyQkbK1XNi9PWW9B5ujRn5Cffw1lZaVITKy/2JKQsM1sDI6yZ2z5+dcaxGUcu6W6pm0nJGxrMA5LfZrWtbZQZdx+WVkpyspKzcZnL9O+duzYYrF/a3Ht3LkV+fnXzI7ZmLkvhvLylHZESr4kOLgVIAUuVV/C/Nz5SL2Z2mChSq9GrEHqzVQ8mfskLlZfhEQqqatPXqNGU4tdp9IAALGxsWjTpo3NOsHBwYiNjQUAJJ7ahxpNrVtjJKKmY5oruZp+ocrU0aM/mclbttbLPfT5qDP5iOn+/PxrAMznhnl5SrM5r3E5Szm0NY3JJ83VNx6naVumv9sTr7m50M+TM+w97nC2vKtY6tdT8RAR+bIWs1iVklJ3u5yFCxdi6NChhu1yuRwrVqxA+/btcerUKeTn5xv2ffzxx5DJZFizZg06dOhg2D5ixAjMnDkTHTt2RHZ2wzOBiIiImquEhG2orKw0/F5ZWWn2WQTmyq1a9U69bcaqqqrqlX377dcblFm16h0kJGyDSqWqt12lUrnkeQj2jG3Vqnca1DOO3VJd07ZVKlWDcVjq07SuPfRzkpCwzWx8zqqurnaqXlVVFVatesfsmG15++03nOqTvJcgE1CiLkHsuViUaOw78atYU4zYc7EoVhdDkLWYQxifsE/5DW5VlSI8PBxTp061u15UVBT69++PW1WlSFN+674AiahJmcuVPKW6uqpe7qHPR53JR0zpx2luvG+//YbN3M1SDm1NY/JJc/WNmbZl+rs98Zqbi8a8H+w97nC2vKtY6tdT8RAR+bJmcaSXkpKCOXPmYNiwYRg0aBCmTJmCTZs21ftCRS6Xo1+/fhg5cmSD+nK5HF27dgUAFBYWAgCUSiWuXr2K0aNH17s1oN5f//pXfP3113jggQfcNCoiIiLvcuXKZWRkpDXYnpGRhqtXr9gsZ1zGloqKCrP109P3mS1vGoOj7BnbkSM/OtSHvq6ltm3VsxaXPdLT91mcL09w9vWpqCg3PE+BfF/btq0hE2TYcGOD3QtVeiWaEmy8sREyQYa2bVu7KUJyRGFFCTJyvwMAxMXFQSqV2l1XKpUiLi4OAJCe+x2KKnjHCiJf52iu5Amuiu/q1SvYuvVfZturqCi3qw1H8tfG5JPO1LenTWOWXvurV684dYtne487nC3vKtb6NZeHuzseIiJf59OLVTqdDsuXL8dLL72E7OxsDB06FKNHj0ZhYSHef/99zJkzBFyD8wAAdPpJREFUB2VlZQCA559/Hnv37sV9993XoB2VSoXz588DADp16gQAyMnJAQAMGjQIOp0O//3vf/H222/j73//O3bu3OmSM3GIiIh8hSiKiI//1PBwaWM6nc6wz1o5d9LpdNi82bl+7RmbVqvFhg1rnYjpE4fnQ9+n8bw6o6lfA3f6978/+/0B5eTrZDIZyjXlOHjroFP1D9w6gAptBWSyFvXoXa+VeDIVGp0WkyZNwqBBjj93avDgwZg4cSI0Og0STnnP4joROU6n0zmcK/m6L77Y3aj69uZ6zuTX7sjPLcVr67XfsGENdDqd3f3Ye9zhbHlXsdWvOe6Mh4ioOfDpxapt27YhNTUVPXr0QFpaGj7//HOsX78e//3vf/Hggw8iJycHr7/e8DZCpj799FOoVCoMHDgQd911FwDg6tWrAACFQoGnnnoKS5YswZYtW5CYmIg33ngDkydPxoULF9w6PiIiIm9x/PgxZGaetLj/1KkTOH78Z5vl3Ckzsy4GR9kztrVrV9l9lmz9mE46NR+nTp1AYuJ2j82lt9FqtVi7drWnwyAX0EGH5JJki8+osqVGrEFycTJ0sP9LL3KPrF/zcOqGEgGKACxdutTpdp5//nkoFAqcyj+D7F/PujBCImpKiYnbncqVWjp9Dm2Ns/m1O/Jzc/Haeu3Ly8uxa9cOu/uw97jD2fKu0tjXhYiIGvLpUxLj4+MBACtXrjTcxg8AAgMDsXr1ajz00EPYv38/4uLi0LlzZ7NtHDp0CJs2bYIgCIbbUAB1H6YAsH79esjlcqxZswb3338/iouLsXbtWqSnp2Px4sVITU1F69a8DQkRETVfarUa8fGf2Sy3efMnTRCN7RgGDx4KuVxuV3l7x/b99981NjSHJSX9p8n79Gbff38I339/yNNhUCNkZGRAKpEiR5XTqHZyVDmQSqQICJBi/PjxLoqOHKHRarDz5JcAgGeefQahoaFOtxUaGopnnnkGH374IXac3Iu3OrwAmdSnD1O9QkzMRE+HQER2io//1GL+am+uaok78nPjeKuqVHblrHv27EJUVAz8/RVWy9k7Xn0MdT/bX97eYwRbGvu6uDoeIqLmwmevrPr111+Rn5+PTp06YejQoQ32BwUFYcyYMRBFET//bP6MhW+//RZLly6FVqvFiy++iFGjRhn21dTUne1ZXl6OdevW4bHHHkObNm3Qq1cvrFmzBoMGDcK1a9ewd+9e9wyQiIiIiKgZ0d+6T6Vt3O209fV5K8DmRwKJp0MgIiIiIiIP8dnFqsLCQgBAly5dLJbRX21VXFzcYN/u3bsRGxuLmpoaxMbGYtGiRfX2KxR1Z3v0798fw4cPr7dPIpFgxowZAICjR486PwgiIiIfIJfLsWDBMzbLLVy4CAsXPtsEEVmPwZEzFO0d2x//OKYxYTklJuaJJu+TyJ00Gg0AQCG1fla1Lfr6+vao6cmkMsweOgVA3S3VzR1v2auoqAiffVZ3dvrsoVN4VRURtTgLFjxrMX+1N1e1xB35uXG8/v4Ku3LWadNm2LyqCrB/vPoYHC3vKo19XVwdDxFRc+GzRwL6hxFKJJbPvtM/iNvPz6/e9jVr1mDjxo2QSCR49dVXsWDBggZ127VrB8DyYpj+toK3b992NHQiIiKfM3z4vRg8eKjF+7IPGTIMw4ePBACr5dxp8OA7MTjCnrEtWxaHU6dOOvwsBv3tSRydjyFDhmHmzLk4ezaXz60CIJVKkZCQDKlU6ulQqBEqK7Vo5a9FpCISB28ddLqdSEUktKIWlZVaJCWlOVyft0dzjYF39cOQzuE4dUOJdevW4c0333SqnXXr1kGlUmFIlwgMuKuvi6NsuZz52yD3CwsLAgAUFTW/ZzvpdDosWDCbz61ykHEObYmtXNWetl2Vn5uLd+bMudi/P9Xiax8UFIQZM+bY3Ycjxx3OlHcVV7wuRERUn89eWdWhQwcAwLVr1yyW0e8LCQkBULfA9dprr2Hjxo3w8/PD+++/b3ahCgD69esH4M4VXKaKiooA3FnUIiIias4kEgkWLlxk9iQRQRCwYMGzkEgkVsu5kyAIWLjwWaf6tWdsUqkUsbEvOBHTIofnQ9+nvr6zc9nUr4E7PfXUM1yoaiYECIgOiUYrSSun6reStEJ0aDQE3z2MaVZmDp0MmSDDvn37kJmZ6XD9zMxMpKWlQSbIMGvIJDdESERNRRAEh3MlX/f44427Ct44h7bGmfzaHfm5pXhtvfZLlrwAQbD/c9ve4w5ny7uKrX7NcWc8RETNgc8e5XXu3BldunRBQUEBTp5seBZDeXk5fvjhBwiCgBEjRgAAVq5cid27dyMwMBCff/45Jk60fFblqFGj0KpVK2RnZ5tdEDt8+DAAGNomIiJq7rp1647x4xt+do4fPxHdunW3Wc64jC2BgYFm60+YYP7LTNMYHGXP2EaNus+hPvR1LbVtq561uOwxYcIki/PlCc6+PoGBQZg8OcrF0ZCnaDQaBMmCMLbdWKfqj2s3DoHSQN4C0Et0CAzB+P6jAQCrV6823NnCHlqtFqtWrQIATOg/BmGBIW6JkYiajqO5kie4Kr5u3bpj/vyFZtsLDAyyqw1H8tfG5JPO1LenTWOWXvtu3bpj1Kj7HO7L3uMOZ8u7irV+zeXh7o6HiMjX+exiFQA89dRTAIBXXnkF169fN2yvrKxEXFwcKioqMG7cOHTo0AHfffcd4uPjIZPJsGnTJtx7771W2w4KCsL06dOh0+kQFxdX73Z/aWlp2LdvH9q3b291wYuIiKi5mTVrHgICAgy/BwQEYObMuXaVi4tbUW+bMX9//3plX3ut4e2k4uJWYNaseYbnSuopFAqzMTjKnrHFxa1oUM84dkt1TdtWKBQNxmGpT9O69tDPyaxZ88zG56zWrVs7Vc/f3x9xcSvMjtmW1157w6k+yTvdvl0NjU6DJZ2XIETm2OJEqCwUz3V+DhqdBrdvV7spQnLUpPCH0M4/GEqlEnv37rW7XkpKCnJzc9FeEYyJ4Q+6L0AialLmciVPad3av17uoc9HnclHTOnHaW68r732hs3czVIObU1j8klz9Y2ZtmX6uz3xmpuLxrwf7D3ucLa8q1jq11PxEBH5Mp9erJo/fz4ee+wxXL58GRMnTsTTTz+NP//5z3jkkUfwzTffICIiAm+88QYA4KOPPgJQd0vAhIQE/OUvfzH734ULFwztL1++HMOGDcPJkyfx6KOP4rnnnsP06dPx4osvQi6XY+XKlWjTpo0nhk5EROQRbdoEY8aMOwdZM2fORZs2wXaV69Ll7nrb9EaNug+zZs2vV7Zfv3Dce+999cp06XI32rQJxsyZ8+rVnzVrntkYHGXP2Lp0ubtBXMaxW6pr2vasWfMajMNSn6Z1jfu3RD8nbdoEm43PXqZ9zZnzpMX+rcU1e/Z8dOlyt9kxGzN35m2/fuF2REq+RKfRIVQeivV91tu9YBUq+728PARatf1X75D7tZL5YcaQuhP41q9fj7KyMpt1SktLsX79egDAjMGT0ErmZ6MGEfkK01zJ1dq2bW92+6hR95nJW+bXyz30+agz+Yjp/i5d7gZgPjfs1y/cbM5rXM5SDm1NY/JJc/WNx2nalunv9sRrbi708+QMe487nC3vKpb69VQ8RES+TObpABpDEAR88MEHGDNmDHbt2oUTJ04AAHr06IFFixZh3rx58PPzQ1VVFbKysgAABQUF+PLLLy22+cQTT+Cee+4BUHfWw7///W9s2bIFKSkp+OGHHxAQEICxY8fiueeeQ2RkpPsHSURE5GUmTJiE6uqq33+e7FA5/bbaWjXy8s5AIpFg2bI4SKXSBmWXLVuOlSv/8fvPcfXaraysgFKZg4iIAVZjcMfYTOPSx67V6iCR1OUn5uqati2Kol31TOtOnhyN1NRkaLVa6HQicnNzoNPpANS1ERk5sF47EyZMQllZKQ4f/hZSqYBHH30UVVVq5OaegSiKkEgk6N27D3744TBu376N3r17Y8CAwZDJZJg8ORopKXvqzfUjj4zDypX/gCiK6Nu3P86dy0NExABMnRpjKNunTz/k5SlRXFyEMWMeqvf6V1ZWIivrFEpKihEaGob+/SMMbURFxWD37gQkJf3H8RePfEZpaQ3atfNHr9a9sLX/Vmy8sREHbh1AjVjToGwrSSuMazcOz3V+DiHyEEALlJXVeiBqsmbk3YPw7fkjyCu6hE2bNiEuLs5q+U2bNqG0tBT9OvTCiLsHNlGURNRUli1bjrlzpxt+nzlzLkRRhFarweHDhyCKIjp06IiIiAEQBAEajRZnzmTh6tUr6NatOwYMGAStVoPvv/8OFRUV6N69O6RSGSIjB2LcuAlYsSIOoijiD3+4H0eP/oiwsA5YtiwOoqjDO++8WS//0OdbAEzykbpcsl+/CEilgiEnEgQBU6ZE48svk6HV6qDRqPH993UxA0DHjp3q5aX68ZrmrPrcTavVGtrV53DGsTiqMfmkaX39OE3bsvS7PSzl786y97jD2fKuYqlfT8VDROSrJKL+E5e8Sm2tBqWlVZ4Ow6XCwuru21xUVO7hSIjIGfwbJvJ9vvJ3PGfONFRXV6F1a3/s2LHH0+GQmwQHt4IgEyATZKjQViC5OBk5qhyotCoopApEKiIRHRpd94wqnQZatdYlC1UxMXdu4/3R1Ncb3R7VuXb7V7x54EMIgoDt27ejd+/eZsudO3cOc+fOBUTg9XHPo2vbTk0cafP15713bqGblJTmwUjIEl/5HHYFfpZTc9WS/o6JmiP+DTcd/Vw7wqevrCIiIiKi5mfmzLnYuzcJU6fGeDoUcqPS0rorqdq2bY0AWQBmd5gNqURq2K8VtRAgQK1W8xlVPuDutnfhoXv+gK/P/4RVq1bh448/hkQiqVdGFEWsWrUKOp0Oj/T5Hy5UETVj/CwnIiIiR3GxioiIiIi8SlRUDKKi+OVWS2G8EBUQIIVMJoNGo0FlJZ9N5WuiBozFsauZOH78OL766iuMHTu23v6DBw/ixIkTCGwVgKgBj3ooSiJqCvwsJyIiIkcJng6AiIiIiIgIACortSgtreFClY8KbKVAzKDxAIA1a9agqurObc2rqqqwdu1aAEDMwPEI8FN4JEYiIiIiIvJOXKwiIiIiIiIilxjdcyS6t+uCgoICxMfHG7Zv3rwZBQUF6N6uC0b3HOG5AImIiIiIyCtxsYqIiIiIiIhcQhAEzBk2FQCwdetWXL9+HdevX8fWrVsBAHOGTYUg8DCUiIiIiIjq4zOriIiIiIiIyGV6h3bHfd2H4qcrJ/HBBx9AFEWo1WpIpVL0Du3u6fCIiIiIiMgLcbGKiIiIiIhapD/vfdPTITRboigCAA4dOmTYJpfLOedERERERGQW779ARERERERELiWRSCCT3Tk3UiaTQSKReDAiIiIiIiLyZlysIiIiIiIiIpfTL1CZLlwRERERERGZ4hEDERERERG1GElJaZ4OwWlhYUEAgKKicg9HYr9ffjkGABgx4l4PR0JERERERN6Mi1VERERERETkFlykIiIiIiIie/A2gEREREREREREREREROQxXKwiIiIiIiJqJn755Zjh1ntERERERES+grcBJCIiIiIiagbUajU+/vgjAMDGjZ9DLpd7OCIiIiIiIiL7cLGKiIiIiIjcJiZmoqdDaDHUajU0Gg2AunnnYpX7JSWleToEIiIiIqJmgbcBJCIiIiIi8nGiKBoWqgBAo9FAFEUPRkRERERERGQ/LlYRERERERH5OLVaDQB44IEHMGbMmHrbiIiIiIiIvB1vA0hERERERE1i9OjRng6hWbp9+zaOHTsGPz8/vPjiiwCAI0eOoLa2FgMHDkTbtm09G2Azc/jwYU+HQERERETU7PDKKiIiIiIiIh8liiKUSiUAYP78+ejatSu6du2KefPmAQByc3N5O0AiIiIiIvJ6XKwiIiIiIiLyUfn5+SgvL0fHjh2xYMECw/aFCxeiY8eOKCsrQ35+vucCJCIiIiIisgMXq4iIiIiIiHyQWq3GuXPnAAAvvPAC/P39Dfv8/f2xbNkyAMC5c+f4/CoiIiIiIvJqfGYVERERERF5tWnTpiE8PBwymQwajQZKpRJ79uzxdFged/78eajVagwfPhyPPvpog/1jx47F7t27ceLECVy4cAH9+/f3QJRERERERES2cbGKiIiIiAxSUpKwd28Spk6NQVRUjKfDoRbuzTffhEwmgw46SCVSAICfnx+GDBuCYcOGQaPR4PXXX/dwlJ5RXl6Oa9euQSqVIi4uDhKJpEEZiUSCuLg4zJ07F9euXUOXLl0QFBTkgWiJyNX4eU1ERETNTYtbrDp06BC2bNmCrKwsqFQqhIWFYfTo0ViyZAk6derUoHxubi42btyIY8eOQaVS4e6778bUqVMxb948KBQKD4yAiMi91Go1kpN3AwCio6dDLpd7OKLmT61WY8+eXVAqsxEeHolp02Z65bybvjcAGH6fPDkKqakphn2m8Zt7X1VVqfDOO2+iuLgIo0c/iCeemG213uTJUUhJSYJSmYPw8AGIinrc0Kd+35kz2QCAiIgB9eaxqkqFlSv/AQB45ZW/QSaT23yfm9bx9zf/uV9eXoq4uBdQWVmBu+/uDolEAqlUioiIgfVi1Pdjbh71r3/fvv0BSJCXd8YwjqioGKSkfIEzZ7Kg0+kgiqKh/WnTZhjm8t1330JhYQFCQkIRETEQUmndF/tarQZK5RkUFRVApaoEAHTv3hMvv/wa9u37EkplDu65pzd++OEwVCoVqqpU0Ol0SEzczi+/yGPeeOMNCDIBMkGGck05kkuSkaPKgUqrgkKqQKQiEtEh0QiSB+Gtf74FnUaHN954w9NhNxlRFJGbmwsAmD59Onr37m2xbJ8+fTB9+nTs2rULubm5GDFihNmFLSLyHVVVKmzZ8i+Iog7//vdn+Pnnn1BUVASJRIIxYx7ClClRWLnynzh//hyCg9sgNDQM165dQ7du3fGXv7yCNWtWQxRF3HNPH/z00/cAgPvvHwO5XA6pVIoJEybi//7vHVy5cgndu/fAyy+/hvT0NGi1WgASACJqa2tw4MB+qNVq9O7dF6+88lfs25eK7OzTKCoqMMTyxBOzodGoreZ8pvmepdxJ379UKjh1nKLPl4qKCvHHPz6IGTMa5p76eOzNzc2VBWDX8ZQzx13N4VjNm8bgTbEQEVELW6z65JNP8N5770EQBAwaNAghISFQKpVITEzEwYMHsW3bNtxzzz2G8l9//TWWLVuG2tpadO7cGUOGDMHVq1fx3nvvIT09HZ9++ilCQkI8OCIiItdLT9+HnTu3Aqh73sXkydGeDagFSE/fh127tgMAsrIyERgY6JXzbvreEEUYfr948TyOHv3JsM80fnPvq7Vr30NOThYAYPfuBLRp08ZqvQsXzuPYsbo+srIycfnyRcPvxv0DQHb26XrzuHbte8jKygQAfPjhe4iIGGjzfW5a5+WX/2Z2Xv72t1dRWFgAAMjNPVMvBuMY9f2Ym0fj199YdvZpXLlyqd7Y6o8xwDCX2dmnAQCFhQVQKnPMxqp35kw2/v73V3HlymWz/QJAdXWV1TaI3OWtt94CpECJugQbbmzAwVsHUSPW1Ctz8NZBbLyxEWPbjcWSzksQKg/FW2+9hb///e8eirppFRQU4NatW2jbti0WL15ss/zixYuRkZGBW7duoaCgwOxJekTkO9aufQ+iqDP8fubMnc/93bsTcOzYEVy9ehkAUFxcjOLiYgCAUpmD5cufx+3btwDAkDsAwBdf/Mfw8w8/HDbUN80ZzFEqc8yW0ed32dlZVnM+S/meudxJz5njFON8ac+eBAQHN8w99fHYm5ubK2ucI1uL05njruZwrOZNY/CmWIiICBA8HUBTOX/+PNasWQOFQoEdO3YgMTERGzZswIEDBzBnzhzcvHkTK1asMJS/ffs2XnrpJdTW1uLZZ5/FV199hY0bN2Lfvn147bXXkJOTgzfffNODIyIicr2yslLDwRYAJCZuR1lZqQcjav7KykqRmLit3raEhG1eN++m742EhG314jZeTDF935h7X+XlKQ1fROjt3LnVaj3T8sa/m1vM0c9jfv61BmUTEu584WHufW6uTn7+tQZ95OUpDV/mmGPcRmLiduTnX2swj8axmGNubMb1zc2lPax96UTkKW+88QYgBS5VX8L83PlIvZnaYKFKr0asQerNVDyZ+yQuVl+ERCppEVdXaTQa5OXlAQBiY2PRpk0bm3WCg4MRGxsLAMjLy4NGo3FrjETkPqY5ijnWchP9QpUj9e3JGSyV2bFji9Wcz1q+Zy53Mt7nSL5sbt5Mc099PPbm5ubK7ty5td42S3E6c9zVHI7VvGkM3hQLERHVaTGLVSkpKdBqtVi4cCGGDh1q2C6Xy7FixQq0b98ep06dQn5+PgBg//79KC8vx7Bhw7B8+XLDrXQA4Mknn8To0aORkZEBpVLZ5GMhInKXhIRtqKysNPxeWVmJxMSGB4fkOgkJ26BSqeptU6lUXjfvpu8NlUrVIG490/eNuffV2283fMZMVVWV1XqO0s/jqlXvmO3LUrwAzNYxt83cOCyprKzEqlXvNJhH41gcpVKpHIqByNsJMgEl6hLEnotFiabErjrFmmLEnotFsboYgqz5H95cunQJNTU1CA8Px9SpU+2uFxUVhf79+6OmpgaXL192X4BE5Fbm8hFvVl1d3WCbcc5nLd8zlzsZ73MkX7aUD5q24Uhubq5sVVVVvW2W4nTmuKs5HKt50xi8KRYiIqrTLI7mUlJSMGfOHAwbNgyDBg3ClClTsGnTpnpJkVwuR79+/TBy5MgG9eVyObp27QoAKCwsBACcPXsWAPDQQw+Zvaf7qFGjAACHDx92+XiIiDzhypXLyMhIa7A9IyMNV69e8UBEzZ+lOQfqbknhLfNuLU5L9O8bS3UrKirM1tOP25k+zdm/P9WueTR+nx858qPZOlevXql3ldOXXyZbHIcl/3979x0fVZX/f/w9JSSZJNQASgchlCAIqOiuoq5KRxAUkLKCBQsq+sNg3bWwq+zCKoKIqAi7iApiBOmCX1dZVgEBgYSAhSJYIKEEkpAw5f7+yM6YMpOZ1JlMXs/Hw4fJPefc+zk3c8jn5tx7bmX8TEsbQ2mEymcQNcNzzz0nq9mq135+LeCJKrcTjhOa+/NcWc3WsF79ICcnR4cP54/LpKSkQjfU+WOxWJSUlCRJOnTokM8bDgCELl85SnW0bt1qbdnyX7/5Xkn9DfQ6paTzVjDnLk1uXppctWicZbnuCodrtVDqQyjFAgD4TbWerHK5XJo8ebKmTJmilJQUdevWTVdffbWOHz+ul156SaNGjdKZM2ckSQ899JA+/vhjXXnllcX2k5OTo++//16SPOu3u1z56z/HxMR4PbbVmv+6rx9++KHC+wUAVc0wDC1c+KYMwyhW5nK5fJah7Eo65+7yBQveCPp59xenLy6XSwsWvFnqPrj7XdV9d3/OnU6nXnvtFZ/1Xnttplwul5xOp/75z7eqLL5gefvteUH/DKLmsFqtOus4qw2nNpSp/SenPlGWM8uTp4ej/fv3y+VyacCAAerSpUup23ft2lX9+/eXy+XyLCUIoHpwuVwl5ijVjWEYmjPnlXLlGYFcp/g7b4Zh6O233/C7r4K5eWnz44L7Lst1Vzhcq4VSH0IpFgBAYdV6suqdd97RqlWr1KpVK61Zs0bz58/XnDlz9Omnn+raa69VamqqnnnG/9I4b775pnJycnTxxRfrwgsvlCS1bt1akvT11197bbN9+3ZJ0smTJyuoNwAQPNu3b9WuXTt9ln/zzQ59+WXp34kD3/ydc0natWuntm/fVkUReRdInL7s2rVDu3d/U4Z2O8vUrry++WaHZs6crqyssz7rnD17VkuXvquZM6d7bmwJZ7t3fxP0zyBqhmHDhskll5afWO7zHVX+5Bl5Wp6xXC65NGzYsAqOMPgyMjKUnp4um82mBx98sMz7eeihh2Sz2ZSenq6MjIwKjBBAZVqyZHGJOUp1VBH9+eabHSXmKoGct927d2rp0sUB5+ZlyY/dcQZy3VW0P2VpE2pCqQ+hFAsAoLBqfdvhwoULJUnTpk3zLOMnSbGxsZoxY4auu+46rV27VklJSWrSpInXfXz++eeaN2+ezGazZ1kMSerfv79eeuklrVmzRr/73e906623esqSk5O1YUP+HZ/nz5+vhJ4BQNWx2+1auND/EyKvvvqq16VUUXp2u10LFrwZUN0FC95Q167dFBERUclRFRfoZyOcbN78hd86H364RE6nswqiCQ0vvPBssENADdCxY0dZTBal5qSWaz+pOamymCzq2LFjBUUWGlwul/bt2ydJuvvuuxUfH1/mfcXHx+uuu+7SrFmztG/fPv3ud7+T2Vyt72EMqqFD+wc7BKDGW7jwTa/58rlzOUpOXhrQPpYtWxJQvbffnuf1VRGBWLDgjYDqFexPoPm4r3MQCkKpD6EUCwCguGp7VfLLL7/op59+0gUXXKBu3boVK4+Li1OvXr1kGIa2bfN+R8S///1vPfjgg3I6nXrkkUc876GSpEaNGun555+X2WzW008/rX79+mnixIkaNGiQnnzySY0aNUqSwnqZEQAAAKAquHPqHGf53qPkbk+ODgAAAADVS7WdrDp+/LgkqWnTpj7ruJ+28ra8xbJlyzRx4kTl5eVp4sSJmjBhQrE6Q4YM0b/+9S/9/ve/16+//qovv/xSDRo00IIFCzRo0CBJUu3atSuiOwAQNBERERo37i6/9R544AHuLKsgERERGj/+7oDqjh8/IWjnPdDPRjj5/e97+a0zbNiIgOoBCJzD4ZAk2Sy2cu3H3d69v3BhNpvVoUMHSflLmJdn+b709HS99Vb+XeUdOnTgqSoA1d64cXd7zZejo20aOnR4QPu45ZYRAdW74457As7jixo/fkJAbQv2J9B83Nc5CAWh1IdQigUAUFy1veXQ/bLDkh6/di/RU6tWrULbZ86cqblz58pkMumJJ57QuHHjfO7jsssu87rs1bJlyyTJ844rAKjOevS4XF27dvO5dvcll3TXlVdeWcVRhTd/51ySunbtph49grv0YiBx+tK1a3cZhqvU75/q2rWbDMOo8vdWXXJJdz38cJJ27drp890GcXFxGj58lAzD0Jdf/ifs31vVpcsleuaZv5Z5uRtACmyZtLS0NF3S/RIl2hK14dSGMh8r0ZYop+FUWlpamfcRquLj49WwYUOlp6dr9uzZeu6558q0n9mzZysnJ0cNGzYs13KCyJecvCbYIaAaadgwTpKUnl76dzW5XC6NG3dbWL23KjY2rtz9ueSS7iXmyyNGjNbatatKPE6XLt00fPho7d+/L+DcvLT5ccE4/V13Fe1PINdqwb5m8CeU+hBKsQAACqu2t9E1atRIknTkyBGfddxlDRo0kJQ/wfXUU09p7ty5qlWrll566SWfE1VZWVnaunWrZ234or766itJUufOncvaBQAIGSaTSePHT/D6B2mz2axx4+7mj9UVrKRzHkh5VSlrHGazWePH36077rinVG3dxyttu/Jyf84tFosmTnzYZ737739YZrNZFotF48aV7a7a6qSqfw6ouT788EOZZdaQBkMUaYos0z4iTZEaEj9EZpn14YcfVnCEoaF9+/Yym81avXq1du3aVer2u3bt0po1a2Q2m9W+fftKiBBAZTGbzSXmKNWNyWTSxIkPlyvPCOQ6xd95M5lMuuOOCf/LXQPLzUubHxeMsyzXXeFwrRZKfQilWAAAhVXbyaomTZqoadOmOnbsmHbuLH43xNmzZ7V582aZzWZdeumlkqRp06Zp2bJlio2N1fz589W/v++7PI8fP66xY8d6vWMxIyNDGzduVGxsrK6++uqK6xQABFGLFi3Vp0/xfxf79OmvFi1aBiGi8OfrnEtS374DQua8lxSnL+7Pja+2sbGxXtu5+12WY3rTr9/AgM5jwc95z55Xem3TokVL9ez52xOGAwcO9tkPXyrjZ1raGEojVD6DqBkcDofirHG6sd6NZWrfu15vxVpiw24JwIJsNptatswflzNmzPCsJBEIp9Op6dOnS5JatWolm618Sy4CqHq+cpTqqG/fAerZ80q/+V5J/Q30OqWk81Yw5y5Nbl6aXLVonGW57gqHa7VQ6kMoxQIA+E21naySpNtvv12S9Pjjj+vo0aOe7dnZ2UpKSlJWVpZ69+6tRo0a6YsvvtDChQtltVo1b948XX755SXuu02bNmrdurV27Nih9evXe7ZnZWVp8uTJOnfunMaPH6+4uLjK6RwABMHIkWMUExPj+T4mJkYjRowOYkThb+TIMcX+YGiz2ULuvBf9bNhsNp9/6Cz6ufH2uXrqqeI3g0RHR5fYrrTc5zEp6Umvx/IVrySvbbxt89YPX2JiYpSU9GSx81gwltKy2WyligEIZc8884wcLofub3K/GlgblKptvDVe9zW5Tw6XQ88880wlRRgaWrdurcjISKWlpenjjz8OuN2KFSu0b98+RUVFqVWrVpUXIIBK5S0fCWVRUVHFthXM+UrK97zlTgXLSpMv+8oHi+6jNLm5t7rR0dGFtvmKsyzXXeFwrRZKfQilWAAA+ar1ZNXYsWPVr18/HTp0SP3799edd96pBx54QNdff70+++wzderUSc8++6wk6dVXX5WUvyTg+++/r0cffdTrfz/88INn/y+88IKsVqsmTZqk0aNH6/7779f111+vr776Stdff73uueeeYHQbACpN7dp1NHz4bwn6iBGjVbt2nSBGFP5q166jESPGFNo2cuSYkDvvRT8bI0eOKRR3wSeOin5uvH2u2rfvqMsvL/wetNtuG1tiu6L1C35f8PgFY6xdu46aNm1erO7IkWN9xivJa5umTZsXO0b79h3VokWrYtu9xThixGg1bdq82HksGIs33vpWsL23cxmIli1blboNUNlcDpfiI+I1p92cgCes4q3/qx/RQE574E8aVVdWq9WzhN+cOXN05swZv20yMzM1Z84cSVJCQoKs1mr76mKgxiuao3hTUm5St249v8co2j6QnMFXnVGj/lhizldSvuctdypYVpp82dt5K5p7uuMJNDf3Vve228YW2uYrzrJcd4XDtVoo9SGUYgEA5KvWVylms1kvv/yyevXqpaVLl2rHjh2S8pe1mDBhgsaMGaNatWrp3Llz2rNnjyTp2LFjWrlypc993nrrrbroooskSd27d9e//vUvzZkzRzt37pTZbFbr1q01efJkDR06lIs8AGGpb98Bys0997+vBwY5mpqhb98Bys7OVlpaijp16hyy573oZ8MwDM/3gwYN0cqVyz1l/tpK0qRJk/XCC88pIyNdvXpd57fdwIFDtGLFh0pLS1WnTp11001DtWrVcs/xly//UHv3pkiSEhMvLrS/SZMma9q0qf/7OkkWi8Xv57xoG1+mTn1RSUkPKzs7Sy1atJLJZJLZbFZi4sWFYnQfx9t5dP/827fvKEnat2+vpx+DBw/V8uXJ2rt3jwzDkGEYnv0XPJcvvvi8jh8/poYNG6ljx0RZLBYZhuRyObV3b6rS048pJydbktSqVRs99tjTWrVqhdLSUnXRRW21efMm5eTkKDs7y2dfgcr27LPP6vnnn1ebqDZa1GGR5v48V5+c+kR5Rl6xupGmSPWu11v3NblPDSIaSE55XcI7HDVu3Fj16tXTqVOnNG/ePCUl+f43SpLmzZunzMxM1atXT40bN66iKAFUlkmTJmv06Fs833fq1Fnp6cdlMpl0zTV/0MCBgzVt2lR9//13qlOnjuLjG+rIkR/VqlVrTZ78uF5++e8yDENt2ybov//dJEm66qprFRFhldlsVr9+AzRt2l91+PBBtWrVWo899rTWrl0lp9Mpk8kkwzB0/vx5ffLJGtntdrVr116PP/4nrVr1sVJSdis9/Zgnlr59B+r663uXmPMVzfd85U7u45vN5jLly+58KT39uM/c0328QHNzb3UL5sj+2pb2uiscrtVCqQ+hFAsAQDIZhmEEOwgUd/68Q5mZ54IdRoVq2DB/ycT09LNBjgRAWTCGgeovkHE8atQw5eaeU1RUtN5998OqCg1hbOjQ394JEej7Xp999lmZrWZZzVZlObO0PGO5UnNSlePMkc1iU6ItUUPih+S/o8rlkNPurDETVW5nz57Vl19+KYvFosWLF6tt27Ze63333XcaPXq0DMPQFVdcwTLmFWDTpk2er5OT1wQxElQ3FZlP8/saCA6ui4HqjTFcddznujR4NAgAAAAeI0aM1scfJ+umm4YGOxTUYO6lvJ977jnFWGN0W6PbZDFZPOVOwymzzLLb7WH/jipf4uLi1Lx5cx05ckTTp0/X66+/LpPJVKiOYRiaPn26XC6XWrRowUQVEEb4fQ0AAMINk1UAAADwGDx4qAYP5g9fCA3uiahhw4apY8eOslqtcjgcSktL04cf8iRB27Zt9euvv2r79u3auHGjbrzxxkLlGzZs0I4dOxQREeFZ6hxAeOD3NQAACDdMVgEAAAAIaUxMeRcREaF27dpp7969mjlzpq666ipFR0dLks6dO6dXXnlFktSuXTtFREQEM1QAAAAAKJE52AEAAAAAAMqmadOmiouL07Fjx7Rw4ULP9gULFujYsWOqXbu2mjZtGrwAAQAAACAATFYBAAAAQDVlMpnUsWNHSdKiRYt09OhRHT16VIsWLZIkdejQodi7rAAAAAAg1LAMIAAAAABUY3Xr1tWFF16oX375RS+//LIMw5DdbpfFYlHdunWDHR4AAAAA+MVkFQAAAIAqsWnTpmCHELYMw5Akff75555tERERnHMAAAAA1QLLAAIAAABANWcymWS1/nYvotVqZfk/AAAAANUGk1UAAAAAEAbcE1RFJ64AAAAAINRxBQMAAACg0iQnrwl2CGGjYcM4SVJ6+lmfdb7+eqsk6dJLL6+SmAAAAACgIjBZBQAAAABhgkkqAAAAANURywACAAAAAAAAAAAgaJisAgAAAKrI119v9SzTBgAAAAAA8rEMIAAAAFAF7Ha7Xn/9VUnS3LnzFREREeSIAAAAAAAIDUxWAQAAoJChQ/sHO4SwZLfb5XA4JOWfYyarqkZy8ppghwAAAAAA8INlAAEAAIBKZhiGZ6JKkhwOhwzDCGJEAAAAAACEDiarAAAAgEpmt9slSddcc4169epVaBsAAAAAADUdywACAADAp+mPXxnsEKq9Az9m6qX521WrVi098sgjkqSvvvpK58+fl9Pp1EtPXRXkCMNP0rQvgx0CAAAAAKAUeLIKAAAAqCQul6EP1nwrSRo7dqyaNWumZs2aacyYMZLyn65yuVgOEAAAAABQszFZBQAAAFSS/+74WUd+OavGjRtr3Lhxnu3jx49X48aNZRiGvtzxc/ACBAAAAAAgBDBZBQAAAFSC7By7Vn56QJL08MMPKzo62lMWHR2tSZMmSZJWfnpAOed4fxUAAAAAoOZisgoAAACoBKs/O6jsHLt69OihG264oVj5jTfeqO7duysrx67Vnx0MQoRA6ImJsahOnUjFxFiCHQoAAACAKsRkFQAAIWjFimTdeecYrViRHOxQAJTBT79madO2o7JYLEpKSpLJZCpWx2QyKSkpSWazWZu2/aSfjmUFIVIg+OrWjVJ8fKzqx9tks9lUq1Yt2Ww21Y+3KT4+VnXrRgU7xGL4PQ0AAABULGuwA6hqn3/+uebPn6+UlBS5XC61adNGQ4YM0ejRo2WxFL9774YbbtCRI0d87i81NVVWa407jagB7Ha7li9fJkkaMuQWRUREBDmiihVI/8pzDgJt661ewW0DBw7WihUfKS0tRR07JmrYsBEVHmt5lBS/0+mUZJLL5VRaWqrMZrMeffRxrVu3xmecdrtdH364VGlpKWrbNkH79+/TiRPpuuqqazR06K1aseIj7d27R4ZhKDHxYg0bNkKSfPa9aHzuuu7YLBazzzgK/gxWrVohp9Mpp9Ol/fv3SpLat+8oi8Xq2Udubo6Skh5WdnaWmjdvoZMnT6pBg3h17NhJ+/bt0/Hjvyo7O1t5ebkym82qU6euzGaz4uMbSjJ0+PAhmUwm9e7dXyNHjtGSJYuVm3tOS5Ys1uDBQyvuhwag0hmGoQ/WfivDkG655Ra1bdvWZ9127drplltu0dKlS7Vszbd6aFw3rxNbQDiqUydSZqtZVrNVZx1ntfzEcqXmpCrHmSObxaZEW6KGNBiiuIg41WtgksvhUmZmXrDD1rlzOfrnP9+SJP3zn2/pzJnTioiIlNPp0P79aXK5XDIMQydPntDvfneVIiIiPfmC5M5FXHI6Hfr2233q2LGzBg++WatWrdD583bt379XJpOpWN7kcNg1bdpUSdLjj/9J0dG2QjG5yx5++FHNnDmjUL38HGuJ0tJSlZDQURaLpVCO9vjjf5Ikzz4effRxrV69UmlpqWrXLj8nc9dzOOxKSnpYWVlZiomxSTKpUaPG6tAhURaLWRaLxW9+VfRcSEaxdkVzSvc5LOk8FD2Ot1zafVx3HP7i9JdX+6tfsPzuu8cH5boq3K/tAABAeKhRsyzLli3TU089JbPZrEsvvVQxMTHauXOn/vrXv+rzzz/XvHnzCk08nT17VkePHlV8fLyuvPJKr/s0m3k4DeFp3brVeu+9RZLy36sxcOCQ4AZUwQLpX3nOQaBtvdUruO3Age+1ZcuXkqQ9e3YpNja2wmMtD3/xF/WnPz2hH3885DPOdetWa+nSxZLy++v24YdLdPToj55zIUmpqXsUGxsrw5DPvheNr2BdN19xuOv98MP32rr1SxWVkrK70D42bvxEx48fkyTt25cmSTp+/JjS0lKLtXW5XDpxIkOSlJ5+vFDZRx99oHr16ik395wkef4PoPrYkXpc3x86rbp16+qee+7xW/+ee+7R+vXr9d2h09qZelzdOzeugiiB4KpXL1qySCfsJ/Taz69pw6kNyjMKT0RtOLVBc3+eqxvr3aj7m9yv+Ih41asXrVOngvu78ZVX/lHo+48+WuazbsEyX7nInj27dPjwgUJ5jlQ8b0pJ2ePJj2bN+ocee+xPhWJyl02ePEmnT58sVC8/x3rXc7yiZs36hwzjt7KCxy5Yf9asf+iXX37x5Dw5OdmS8vOZ1NQ9hfpaUn4VSF7mLaf0dx6KtiuYx7lz6ZJyR29x+sur/dUvWN6wYT3deuutJe6vMoT7tR0AAAgPJsMwjGAHURV+/fVX3XDDDbJYLFq4cKG6desmSTpz5ozGjx+vlJQU/eUvfymUOG7dulVjx47Vrbfeqr/85S9VGu/58w5lZobXHygbNoyTJKWnnw1yJPDnzJlMTZx4l7Kz8y8+Y2JiNGfOW6pdu06QI6sYgfSvPOcg0Lbe6k2b9pIef/z/ebYVZbPZ9Npr8yss1tIoOobLEn9BReM8cyZT999/p3JycgKOKTo6WiaTydOm4D6Lxmez5d91W3T/3uIo2C4QUVHRFTqpFBUVpdzcXM/3yclrKmzfqNkC/V08dGh/z9fTH/d+ww68yzvv1NTZX+n0mTw99dRTuvnmmwNql5ycrBdeeEF1a0fqTw9eochavK+nPJKm/fZH/3D6NzRc8uk6dSJljjDrYO5BTfxuok44TvhtE2+N16vtXlWbqDZy2p1Be8Lqp5+O6MEH/U9Ce+MrFwlEdHS0zp0rnGvMnj1PTZs29xvTiy/+Q1On/qlMxy0rf/mVv7xMktd8rKTz4O04RZWUO3pr7y+v9le/aHlsbKzee+892e1V9298uF/bAVUtXH4XAzUVY7jquM91adSYx4LWrl0ru92u4cOHeyaqJKl27dq66667JEmbNm0q1Gbv3vylnhITE6suUCAEvP/+O4Uu8LKzs7VkyeIgRlSxAulfec5BoG291Zs+/YUSJ0lycnIqNNbyKEv8BRWN8/333yn1H1HOnTtXqE3BfRaNLycnx+v+vcVRmokqqeKffio4UQWgevlk0yGdPpOnjh076qabbgq43eDBg9WhQwedPpOnDZsOV2KEQPCZrWadsJ8IeKJKkjIcGZr43URl2DNktgbvMnb69BfK3NZXLhKIohM0BWPxF9Nf//pslU5USf7zK395ma98rKTz4O043tr7yh29tfeXV/urX7Q8KytLCxYs8Lm/yhDu13YAACB8hMVk1YoVKzRq1Ch1795dXbp00aBBgzRv3rxCf+y7/fbbtXHjRt17773F2rsTt6LvnkpLy1/Gickq1CSHDx/S+vXF70Bev36Nfvyx+v/xLJD+leccBNrWV71AzvG6dasrJNbyKE/8Bbnj9LW/sli/fo2++uq/pdpfZcQBoOZJP5mjTzf/KElKSkry+j5UXywWi5KSkiRJG//7ozJOhtcT9oBb3bpRspqteu3n1wKeqHI74TihuT/PldVsVd26UZUUoW9fffXfkMqHf/zxsBYtWuA3pqys4Nw5XNb8at261aWq/+OPh7Vly5dlzuP8xekrr/ZX31f58uXLq+xzFO7XdgAAILxU68kql8ulyZMna8qUKUpJSVG3bt109dVX6/jx43rppZc0atQonTlzRlL+u6WaN2+uBg0aFNrHoUOHNHfuXEkqdvfr3r17ZbFYdPDgQY0ePVqXXnqpLrvsMt17773avXu3gHBjGIYWLnxT3lYHdblcPsuqi0D6V1I//Z2DQM9fSfUC7ceCBW+UK9byKG/8BblcLi1Y8IYWLKi4WF0ul1577ZVS7S8/jje1YMEb1fozDiC4ktd9L4fT0IABA9SlS5dSt+/atav69+8vh8Ol5PXfVUKEQPBZrVaddZzVhlMbytT+k1OfKMuZVexGw8rmcrk0Z87MKj1mID766INgh+CTO88rbd7ozpdLY86cl8ucT/rLA73l1f7y/tLurzKE+7UdAAAIP9V6suqdd97RqlWr1KpVK61Zs0bz58/XnDlz9Omnn+raa69VamqqnnnmGa9tZ8yYoZEjR6pfv346efKk/vznP+vaa6/1lJ8/f14HDhyQ0+nUlClTlJeXp549e6pevXr67LPPNGrUKK1bt66KegpUje3bt2rXrp0+y7/5Zoe2b99WhRFVrED6t3Tp4jKfg0DPn796gdi1a2e5Yi2Pioi/oF27dmr37orbn1S2O4h37dqh3bu/qdA4ANQce787oT37MxRjs+nBBx8s834eeugh2Ww27d6Xob3fl+6pE6A6cMml5SeWK88o2zun8ow8Lc9YLpdcFRxZyZYsWazs7KwqPWY42LVrZ4Xmjb5kZWWVK5/0lwcWzav95cOl3V9lCPdrOwAAEH6q9na0CrZw4UJJ0rRp09SsWTPP9tjYWM2YMUPXXXed1q5dq6SkJDVp0qRQ2+XLlys9PV2SZDKZdPDgQeXm5ioqKn85if3798vhcPzv5aNzdOWVv71cfOHChXrxxRf1xBNPqEePHmrYsGEl9xSofHa7XQsXvuW33sKFb6pr126KiIiogqgqTqD9+/BD/3enejsHge5/wYI3/NYJ1LJlS/3WqeifV6D9RMUaOrR/sEMAUAKHw6Vla/OfhLrr7rsVHx9f5n3Fx8frrrvu0qxZs7RszXd68v56sgbx/TzhgH9DQ8f69etlMVmUmpNarv2k5qTKYrIoJsaiPn36VFB0QMnceXX+1+XPhyvzuircr+0AAEB4qrZXvr/88ot++uknXXDBBerWrVux8ri4OPXq1UuGYWjbtuJ3Cy1btkzffPON3nvvPV100UVatGiRHnjgAU/5xRdfrP/85z9auXJloYkqSRo3bpxuuOEG5eTk6KOPPqr4zgEAAKDGM5mCHQFQsdxL9+U4c8q1H3f7ql4KEAAAAEDlqbaTVcePH5ckNW3a1Gcd99NWGRkZxcouuOACRUdHq3v37po/f74aNmyoTZs2aefO3x6Tb9iwoc/9X3fddZKklJSUMvcBCCUREREaN+4uv/XGjbu7Wt55F2j/hg271W8db+cg0P2PHz9B48ff7bdeIG65ZbjfOhX98wq0nwBQk1itZt3Sr50k6c033/SaewYqPT1db72Vfzf8sH7teKoKYcXhcEiSbBZbufbjbu/eH1AV3Hl1ReXDlXldFe7XdgAAIDxV21vR3C8CNZVwy6nT6ZQk1apVq8R91a5dW9dee60++OAD7d271+uTWkW5l/7Lzc0NNGQg5PXocbm6du3mc23zSy7prh49LqviqCpOIP0bPny09u/fV6ZzUJrzV1K9QHTt2q1csZaHv36WVteu3WQYqtD3VsXGxpX6vVVdu3aXYbhC8r1Vyclrgh0CwkTDhnGSpPT0kscHy6aVXqd2DXRx+3jt2Z+h2bNn67nnnivTfmbPnq2cnBx16RCvTm0bVHCUNVM4/Rsa6BgOVdnZTkVGO5VoS9SGUxvKvJ9EW6KchlPZ2c4q+/m6XC7dfvtI3ltVSu5l8yr7vVWxsbFq06ZdmfNJf3lg0bzaXz5c2v1VhnC/tgMAAOGn2t6q2ahRI0nSkSNHfNZxlzVo0EBLlizR5MmTtX//fq913RNa7rvz1qxZo8mTJ2vlypVe6x89elRS/hNaQLgwmUwaP36C10lgs9mscePuLnGCONQF0j+z2VzmcxDo+SupXmn6UZ5Yy6O88Rfk7sMdd1TM/tz7nDjx4VLtLz+Ou3XHHfdU6884gOAa2retrFazVq9erV27dpW6/a5du7RmzRpZrWYN7dOuEiIEgs8ss4Y0GKJIU2SZ2keaIjUkfojMVXwpazab9cADj1TpMQNx883+VwUIFneeV9q80Z0vl8bEiY+UOZ/0lwd6y6v95f2l3V9lCPdrOwAAEH6q7WRVkyZN1LRpUx07dqzQ0n1uZ8+e1ebNm2U2m3XppZcqJSVFq1at0ooVK4rVtdvt+vLLLyVJiYmJkqQTJ05o1apVeu+994rVNwxDH3/8sSTpqquuqshuAUHXokVL9elT/I76Pn36q0WLlkGIqGIF0r/ynINA2/qqF8g57tt3QIXEWh7lib8gd5y+9lcWffr0V8+eV5Zqf5URB4Cap2F9m67/XXNJ0owZMzxP+QfC6XRq+vTpkqQbftdC8fWjKyVGINgcDofirHG6sd6NZWrfu15vxVpig7IEYM+eV4ZUPtyiRUuNHTveb0yxsXFVFFFhZc2v+vYdUKr6LVq09PxsypLH+YvTV17tr76v8iFDhlTZ5yjcr+0AAEB4qbaTVZJ0++23S5Ief/xxz5NOkpSdna2kpCRlZWWpd+/eatSokW699VaZTCb961//0pYtWzx18/Ly9Nxzz+nAgQPq2rWrevToIUkaMGCAYmNjtX37di1cuNBT3zAMzZkzR998840SEhL0hz/8oWo6C1ShkSPHKCYmxvN9TEyMRowYHcSIKlYg/SvPOQi0rbd6SUlPFtpWlM1mq9BYy6Ms8RdUNM6RI8fIZivdOyyio6MLtSm4z6Lx2Ww2r/v3FkegfXCLiqrYPypHRUVV6P4AVJ3eV7dS3dqRSktL89zcFIgVK1Zo3759qlcnUjdezR8QEb5On86Vw+XQ/U3uVwNr6Za6jLfG674m98nhcuj06eAsx56U9GSZ2/rKRQIRHV0813DH4i+mp556tszHLSt/+ZW/vMxXPlbSefB2HG/tfeWO3tr7y6v91S9aHhsbq/Hjx/vcX2UI92s7AAAQPqr1ZNXYsWPVr18/HTp0SP3799edd96pBx54QNdff70+++wzderUSc8++6wkqUuXLpo0aZLsdrtuv/12jRw5Uvfee6+uv/56ffDBB2revLlmzpzpeQy+fv36euGFFxQREaEXX3xRAwcO1EMPPaS+fftq9uzZatiwoWbPni2rtdq+9gvwqXbtOho+/LcLmBEjRqt27TpBjKhiBdK/8pyDQNt6q9e0afNC23r2vLJQm5Ejx1RorOURSPxFtWjRqlD9gnHWrl1HI0aM8dm26LmQpNtuG1uoTcF9Fo1v5MgxXvfvLY6C7S6/vPhxixo1amyhvpXXqFF/rLB9AahakbUsurlPW0nSnDlzdObMGb9tMjMzNWfOHEnSzb3bKrKWpVJjBILN5XApPiJec9rNCXjCKt76v/oRDeS0B/7UYkVr2rR5QLmBN75yEcl7nlMwt7jttrGFjtuz55Vq2rS515jq1q1fqF779h1LzLHc9Qruw1dek/8Ek/eygvzlV/7ysqL13Uo6D96OU/RnVVLu6K29v7zaX/2i5XfccYfq1q3rc3+VIdyv7QAAQPio1jMtZrNZL7/8snr16qWlS5dqx44dkqRWrVppwoQJGjNmjOddVJJ03333qVOnTlqwYIH27NmjvLw8NWvWTMOGDdOdd96p2rVrF9p/nz591LRpU82bN0/btm3ToUOH1KhRI40dO1b333+/6tevLyBc9e07QLm55/739cAgR1PxAulfec5BoG291Su4bdCgIVq+PFlpaSnq1KlzpcRaHiXF73Q6ZTKZ5HQ6tW/fXplMJj366JNat26Vzzj79h2g7OxspaWlqF279tq/P00ZGenq1es63XzzLVq+PFl79+6RJCUmXqy+fQfKMAyffS8an7uuOzaz2ewzDne7gQOHaNWq5XI6nXK5XNq3b68kqUOHTrJYLJ59XHPNdUpKeljZ2Vlq3rylTp48ofj4hurYsZPS0tJ0/Pivys7OVl5ersxms+rUqSuz2ayGDRvJMFw6fPiQTCaT+vYdqL59B+rtt98o988HQHB0T2ykTdt+0veHTmvevHlKSkoqsf68efOUmZmpdq3qqltioyqKEgiezMw81asXrTZRbbSowyLN/XmuPjn1ifKMvGJ1I02R6l2vt+5rcp8aRDSQnNKZM+eDEPVvJk2arNGjb/F8P2zYcFmtEXI4HNq/P00ul0uSdOJEhn7/+16KiIjw5Au/5SIuuVxO7d+fpk6dOmvw4KFauXK5zp+3a//+/LwpKelJrV37W950/fW9NW3a1P/FkFQsJnfZI48k6eWXpxeql59jZSktLVXt23eUxWIplKNNmpQkw3B59pGU9KRWrVqhtLRUtWvXXt9+u89Tz27PU1LSw8rKylJMjE2SSY0aNVbHjp1lsZgDyq+KnguTScXaFc0p3eUlnYeixxk4cIhWrCicS5eUO3qL0x9/9QuW33zzzX73VxnC/doOAACEB5NhGEawg0Bx5887lJl5LthhVKiGDfPXSk9PPxvkSACUBWO4ao0aNUy5uecUFRWtd9/9MNjhIEwEOo6HDv3t/RbTHy/bEwQ13U+/Zmna61tlNlu0ePFitW3b1mu97777TqNHj5Zk6LF7L1PTxrFVG2iYSpr2pefr5OQ1QYykYoXb7+I6dSJltpplNVuV5czS8ozlSs1JVY4zRzaLTYm2RA2JH5L/jiqXQ067M+gTVW78nkZZhNsYBmoixjFQvTGGq477XJdGtX6yCgCAcDVixGh9/HGybrppaLBDAVAGTS+I1dWXNdMXW49q+vTpev311z3LTbsZhqHp06fL5XLpmp7NmKhCjZOZmf8kVd26UYqxxui2RrfJYvptGUyn4ZRZZtnt9qC9o8oXfk8DAAAAFYvJKgAAQtDgwUM1eDB/AAOqswHXtdb2lGPavn27Nm7cqBtvvLFQ+YYNG7Rjxw7F2iI04LrWQYoSCL6CE1ExMRZZrVY5HA5lZwfv3VT+8HsaAAAAqFjmYAcAAAAAhKMYW4QGXd9GkjRz5kydO/fbEs/nzp3TK6+8IkkadH0b2aIjghIjEGqys53KzMwL6YkqAAAAABWPySoAAACgkvyuexM1vzBOx44d08KFCz3bFyxYoGPHjslkMunK7k2CFyAAAAAAACGAySoAAACgkpjNJt3aP0GStGjRIh09elRHjx7VokWLJEkREREym00l7QIAAAAAgLDHO6sAAACAStSmRR1d3vUCbd31q15++WUZhiG73S6LxSKLxRLs8AAAAAAACDomqwAAAOBT0rQvgx1CWDAMQ5L0+eefe7ZFROS/p4pzDAAAAACo6VgGEAAAAKhkJpNJVutv94lZrVaZTCz/BwAAAACAxGQVAAAAUCXcE1RFJ64AAAAAAKjpuEoGAABAIcnJa4IdQtj6+uutkqRLL708yJEAAAAAABA6mKwCAAAAqgiTVAAAAAAAFMcygAAAAAAAAAAAAAgaJqsAAAAAAGX29ddbPUtcAgAAAEBZsAwgAAAAAKBM7Ha7Xn/9VUnS3LnzFREREeSIAAAAAFRHTFYBAAAACAtDh/YPdgg1jt1ul8PhkJR//pmsqjzJyWuCHQIAAABQaVgGEAAAAABQaoZheCaqJMnhcMgwjCBGBAAAAKC6YrIKAAAAAFBqdrtdknTNNdeoV69ehbYBAAAAQGmwDCAAAACAsDP0r28EO4Swln7wW30ye6pq1aqlRx55RJL01Vdf6fz583I6nbJYLJL4OZRX8lMTgh0CAAAAUCV4sgoAAAAAEDCXy6WvP1okSRo7dqyaNWumZs2aacyYMZLyn65iOUAAAAAApcFkFQAAAAAgYD9s+bdOHj2kxo0ba9y4cZ7t48ePV+PGjWUYhpxOZ/ACBAAAAFDtMFkFAAAAAAhIXnaWdq35QJL08MMPKzo62lMWHR2tSZMmSeLpKgAAAAClw2QVAAAAgIDFxFhUp06kYmIswQ4FQbB7fbLysrPUo0cP3XDDDcXKb7zxRnXv3l1S/oQVAAAAAASCySoAAABUmhUrknXnnWO0YkVysENBOdStG6X4+FjVj7fJZrOpVq1astlsqh9vU3x8rOrWjQp2iKgCp37+Ud9t3iiLxaKkpCSZTKZidUwmk5KSkmQ2m+V0OnXq5yNBiBRAWfA7GwAABJM12AEAAFAd2e12LV++TJI0ZMgtioiICHJEVaMi+h2scxfIcX3V8de2pPKCZQMHDtaqVSt81luy5B2tX79WMTExmj59pqKibIXarljxkdLSUtS2bTt99923MgxDHTsmKiIiQjfc0FtPPPGosrKy1LJlS1ksVrVv30kWi/veJJNq147WqFGjZLfb9cEH7+mLLz6TYRiKj2+gjIwTys7OksvlkslkktlsVosWrdSuXYI+/fQTSdIf/tBbBw/+IJPJpEcffVwff7xCX3zxf8rOzpJkUqtWrTV58mP6xz/+psOHD6lly9b6/vtvdf58npYsWazBg4eW+eeH4KhTJ1Jmq1lWs1VnHWe1/MRypeakKseZI5vFpkRbooY0GKK4iDjVa2CSy+FSZmZesMNGJTAMQ19/tEiGYeiWW25R27ZtfdZt166dbrnlFi1dulTbP1qk6+9/wuvEFoDQYbfb9c47C+V0OvTPf76l1atXKDs7W2azSS1bttZTTz0rqzVCy5cvk9PpkpS/zKfL5dL+/Wnq2DFRw4aNUEREhOx2uz78cKnS0lLUunWb/+URJr300mx9+ukGpaTsUUbGcTVoEC/DcOnkyZNq0KCBMjIyZDKZ1KvXdbr55ls8OZM7B0pN3S2TyaQOHTrJbDbLYrFoyJBbJEkffrhUe/fukWEYSky8WIMHD1Vy8jL95z//VsOGjfTEE39WdLStxP4Hksu5j/fBB+9p06Z/Kz6+oaZMeVKrV69SWlpKsfNQtF3BvK408fmKsbLz6kD3X5X5fcHPV8HzXdVq6vUgAFSmGjdZ9fnnn2v+/PlKSUmRy+VSmzZtNGTIEI0ePVoWS/GlTPbt26e5c+dq69atysnJUfPmzXXTTTdpzJgxstlKTiQAAOFr3brVeu+9RZLy39ExcOCQ4AZURSqi38E6d4Ec11cdf21LKi9YduDA99qy5Uuf9ZKT898Dk52dpT//+Qn94Q+9vbbds2eXp11q6h5J0tq1q3X69ElJUlraXklSSsruYn202Ww6ezZXy5a979mWnn68WL38/aQqLS3V8/3KlR95vv7zn5/Q4cOHCtXfuzdFkydP8sSxd+8eT1lu7jmvx0DoqlcvWrJIJ+wn9NrPr2nDqQ3KMwpPRG04tUFzf56rG+vdqPub3K/4iHjVqxetU6f4eYebw99s0fEf9qlu3bq65557/Na/5557tH79eh37IU0/7tqqlpf0rIIoAZTVunWr5XQ6PN9nZKR7vt67N0WzZv1DnTpd7MlLitqzZ5diY2M1cOAQrVu3WkuXLvZsd3v44ft17txvvx+OHz/m9etly97XkSOHPXlPwRxIKpzfREdHyzDkOZ6UnxsdPnzQ0+bYsV81a9Y/9Nhjfyqx/4Hkcu7jufOoY8d+LZQTFT0PRdu5v//hh++1dWvg8fmKsbLz6kD3X5X5fdHPl/t8V7Waej0IAJWpRi0DuGzZMk2YMEHbtm1TYmKirrjiCv3000/661//qgkTJsjhcBSq/3//93+69dZbtW7dOkVFRel3v/udDMPQP/7xD40ZM0YnTpwIUk8AAMF05kxmoQviJUsW68yZzCBGVDUqot/BOneBHNdXHX9tSyovWlbwDy1F67333r8KxXP48KFC2wq29cY9QeTP/Pnzff6hqTSKTlSVNg6Etjp1IiWLdDD3oMbuG6tVJ1cVm6hyyzPytOrkKv1x3x91IPeATBZTfnuEDUdernaufE+SNHHiRNWuXdtvmzp16mjixImSpB0fvytHXm6lxgig7M6cydSSJe+UWGfLli/1/vsl5w/vv/+OfvrpiM99FZyo8qdg3lNSDvT+++94jatomy1bvtRPP3lflrQ0udz7779TLI8qmhO5z0PRdgXPi3uiKpD4fMVY9BgVnVcHmrdXZX7v7bP6/vvvVPm12OnTp2vk9SAAVLYaM1n166+/6tlnn1VUVJTeffddLVq0SK+//ro2bNigzp076z//+Y8++ui3u4VPnz6tKVOm6Pz587r77ru1ceNGzZ07V6tXr9ZTTz2l1NRUPffcc0HsEQAgWN5//x1lZ2d7vs/OztaSJYtLaBEeKqLfwTp3gRzXVx1/bUsqL1pWUNF6ubnF/5DrbVt5ZWdn85QT/DJbzTphP6GJ303UCUdgN2hlODI08buJyrBnyGytMZcZNULKpyuVc/qkOnbsqJtuuingdoMHD1aHDh2Uc/qkUv9vVSVGCKA83n//HeXk5Pit52+yKScnR9OnvxDQvipKTk5OwJNg06e/4HV7aXK5nJwcv3mU+zwUbefvvPiKz1eMRY9R0Xl1oHl7Veb33j6rOTk5VX4t9vbbb9fI60EAqGw15ipy7dq1stvtGj58uLp16+bZXrt2bd11112SpE2bNhWqf/bsWXXv3l2TJ08utETgH//4R1199dVav3690tLSqq4TAICgO3z4kNavX1Ns+/r1a/Tjj4eDEFHVqIh+B+vcBXJcX3XWrVtdYtuS9r1ly3+9lhWt99VX/9W6dasD7Q5Q6erWjZLVbNVrP78W8ESV2wnHCc39ea6sZqvq1o2qpAhRlc5mHFPaZ/n/liUlJXldOt0Xi8WipKQkSdLez9bo7AnvS44CCB5fuUxZhXI+/OOPh4s9cVVSLvfVV/5zuZKOVRHxlRSjt2NUVF4daN5elfl9SZ/VdetWV9ln78CBA1qxYkWx7eF+PQgAVSEsJqtWrFihUaNGqXv37urSpYsGDRqkefPmFbob+fbbb9fGjRt17733FmvvvhvCav3tFV7ffvutJOm6667z+jLgnj3z11wvOMEFAAhvhmFo4cI3ZRhGsTKXy+WzrLqriH4H69wFctySjm8Yhs+2Cxa8qQUL3vBZ/tprr/jtk7teTeFyuYIdAgJgtVp11nFWG05tKFP7T059oixnVqHcGtXXjhXvyuV0aMCAAerSpUup23ft2lX9+/eXy2HXjhXcdQ6EkpLypHA1Z85MTz7iL08MJJerzPik0v+MKiKvDjRvr8r83t95MAzDZ15ekQzD0Kuvvuo1pw3n60EAqCrVerLK5XJp8uTJmjJlilJSUtStWzddffXVOn78uF566SWNGjVKZ86ckSSZzWY1b95cDRo0KLSPQ4cOae7cuZJUaEkL9y+emJgYr8d2X3z/8MMPFd4vAEBo2r59q3bt2umz/Jtvdmj79m1VGFHVqIh+B+vcBXLcpUsXl1jHl127dmj37m98lp89ezag/WRlBVYvHCxd+m6wQ0AAXHJp+YnlPt9R5U+ekaflGcvlEpOT1d3Pabt1NHWHbDExevDBB8u8n4ceekg2m01HU3bo5327KzBCAOXhL08KR1lZZz35iL/+ByNHKxifVLafUXnz6kDz9qrM7wM5D7t27az0a7Ht27dq2zbfxwjX60EAqCrV+nbHd955R6tWrVKrVq00f/58NWvWTJKUlZWlyZMn69///reeeeYZvfzyy8XazpgxQ19//bV27dqlqKgo/fnPf9a1117rKW/durUk6euvv9bo0aOLtd++fbsk6eRJXiIOADWB3W7XwoVv+a23cOGb6tq1myIiIqogqspXEf0O1rkL9LgffvhBhRwP/i1d+i4TViFu/fr1spgsSs1JLdd+UnNSZTFZFBNjUZ8+fSooOlQlp8Ohr5cvkiTdfdddio+PL/O+4uPjddddd2nWrFn6+qNFGpD0oiw8eVdqQ4f2D3YIQFj48MMlGjBgUEB5YjB8+OESDR48VFZrRJljLGteHWj+vGDBG5UaR9GYFix4M6C6Cxa8UWnXYjX1ehAAqlK1frJq4cKFkqRp06Z5JqokKTY2VjNmzFBcXJzWrl2rn3/+uVjb5cuXa+fOnXK5XDKZTDp48GChZQP79++vyMhIrVmzRh98UPiPWMnJydqwIX9ZlPPnz1dCzwAAAICq5149IMdZ8kvg/XG3ZylAFOVtiXUAAAAAqLaTVb/88ot++uknXXDBBerWrVux8ri4OPXq1UuGYXh9RHfZsmX65ptv9N577+miiy7SokWL9MADD3jKGzVqpOeff15ms1lPP/20+vXrp4kTJ2rQoEF68sknNWrUKElcgANATREREaFx4+7yW2/cuLvD6i66iuh3sM5doMcdNuzWCjsmUN05HA5Jks1iK9d+3O3d+0P1Y7FademQsZKkN998UxkZGWXeV3p6ut56K/9u9B5DxvBUFYCgGjZshOLi6gSUJwbDsGEjFB1tCziX9aaseXWgxxw/foLGj7+70uIoGlMgx3LHVVnXYjX1ehAAqlK1vUo4fvy4JKlp06Y+67iftvJ2YXXBBRdIkrp376758+erf//+2rRpk3bu3OmZ/BoyZIiaNm2quXPnaufOnTp27Ji6dOmiJ598UpGRkXr33XdVu3btiu4aACBE9ehxubp27eZzvfRLLumuHj0uq+KoKl9F9DtY5y6Q4w4fPlr79+8r9fsAunbtLsNw+XxvVVxcXEDvrYqNjasx761atmyVzOZqe69UjZCd7VRktFOJtkRtOLWhzPtJtCXKaTiVne1UcvKaCoywZCyTVrGadOyiZonddTR1h2bPnq3nnnuuTPuZPXu2cnJy1KxzdzXp0KWCo6w5qnIsoXI0bBgnSUpPD43f+4Zh6Pnnn65R762KjY3T8OH5Nx/7yxODkaMVjE/yH6M35c2rS5O3V1V+H8h56Nq1W6Vfi/Xocbkuu+wyn++tCtfrQQCoKtX2rwWGYUgqeRkJp9MpSapVq1aJ+6pdu7bnfVV79+4tVHbZZZfp7bff1s6dO7Vjxw4tXLhQV155pQ4cOCBJuvDCC8vaBQBANWMymTR+/ASvv3vMZrPGjbs7LJc3qoh+B+vcBXJcs9nss47JZPLZdvz4u3XHHff4LL///of99slsNmvixIcD71A1x0RV9WCWWUMaDFGkKbJM7SNNkRoSP0Tm6nupgQK6Dx4lszVCq1ev1q5du0rdfteuXVqzZo3M1gh1H1z8XcAAgqekPClcTZz4sCcf8ZcnTpzoP5erzPik0v+MKiKvDjRvr8r83t95qKrPsslk0oMPPug1pw3n60EAqCrV9gqyUaNGkqQjR474rOMua9CggZYsWaLJkydr//79Xuu6J7TcS5VkZWVp69at2rdvn9f6X331lSSpc+fOZesAAKBaatGipfr0KX7nfp8+/dWiRcsgRFQ1KqLfwTp3gRzXV52+fQeU2LakfffseaXXMm/1+vYdEGh3gErncDgUZ43TjfVuLFP73vV6K9YSyxKAYSIuvrE6XttPkjRjxgzPDYGBcDqdmj59uiSp03X9FdegUaXECKDsfOUy5dlfqGrRoqV69ryy2Lby5HIlHasi4ispRm/HqKi8OtC8vSrz+5I+q337Dqiyz17r1q01ePDgYtvD/XoQAKpCtZ2satKkiZo2bapjx45p587ijwGfPXtWmzdvltls1qWXXqqUlBStWrVKK1asKFbXbrfryy+/lCQlJiZKyl9mcOzYsV6XusjIyNDGjRsVGxurq6++uoJ7BgAIdSNHjlFMTIzn+5iYGI0YEf53i1dEv4N17gI5rq86/tqWVF60rKCi9aKioorV8batvGJiYhQVFV3h+0X4OH06Vw6XQ/c3uV8NrA1K1TbeGq/7mtwnh8uh06dzKylCVLXO1w+SrW59paWl6eOPPw643YoVK7Rv3z7Z6jZQ4h8GVmKEAMpj5Mgxstn8v6swOrrk/MFmsykp6cmA9lVRbDab37jckpKe9Lq9NLmczWbzm0e5z0PRdv7Oi6/4fMVY9BgVnVcHmrdXZX7v7bNqs9mq/FrsjjvuqJHXgwBQ2artZJUk3X777ZKkxx9/XEePHvVsz87OVlJSkrKystS7d281atRIt956q0wmk/71r39py5Ytnrp5eXl67rnndODAAXXt2lU9evSQJLVp00atW7fWjh07tH79ek/9rKwsTZ48WefOndP48eMVFxdXRb0FAISK2rXraPjw3y5GRowYrdq16wQxoqpREf0O1rkL5Li+6vhrW1J50bKCd8sWrXfbbX8sFE/Llq0KbfN2p21BdevWL7Hc7c4779Rtt40NqG5JWrZsVa44ENpcDpfiI+I1p92cgCes4q3/qx/RQE574E/fIPRZI6PUbdBtkqQ5c+bozJkzfttkZmZqzpw5kqTuN90ma2TFT74DqBi1a9fRiBFjSqzTs+eVGjmy5Pxh5Mgxatq0uc99BTqp5D6et6+9HdNbXEXb9Ox5pZo2be51H6XJ5UaOHFMsjyqaE7nPQ9F2Bc/L5ZcHHp+vGIseo6Lz6kDz9qrM7719VkeOHFPl12J169atkdeDAFDZrMEOoDzGjh2rnTt3au3aterfv78uu+wyRUdH6+uvv9apU6fUqVMnPfvss5KkLl26aNKkSZo5c6Zuv/12XXLJJapbt65SUlKUnp6u5s2ba+bMmYXWln3hhRc0duxYTZo0ST169FCdOnW0fft2nT59Wtdff73uueeeIPUcABBsffsOUG7uuf99XXPuFq+Ifgfr3AVyXF91/LUtqbxg2aBBQ7Ry5XKf9TIzT2n9+rWKiYnR1Kl/V2RkZKG2y5cnKy0tRe3aJejbb/fLMAx16tRZVqtVN9zQW0888aiysrLUsmUrWSwWdeiQKIvFLMMwZDKZFBcXrZtvvlnHj5/R2bNn9MUXn8kwDMXHxysjI0PZ2VlyuVwymcwym01q0aKV2rVrr08/zb9x5/rre+vAgR9kMpmUlPSkVqz4SF988X/Kzs6WySS1atVG/+//PaZ//GOaDh8+pFatWis1dU/gPySEjMzMPNWrF602UW20qMMizf15rj459YnyjLxidSNNkepdr7fua3KfGkQ0kJzSmTPngxA1KlPLS3rqu/9+quM/7NO8efOUlJRUYv158+YpMzNTjS/qqBZdL6+iKAGUVd++A7RgwRue7+PjGyo7O1tms0mtWrXRpElJslgsys09J6fTJZMp/13iLpeh/fv3qlOnzp7cpm/fAcrOzlZaWopat26jTz/9RJJJL7/8mjZuXKeUlD3KyDiu+PiGMgxDJ05kqEGDeGVkpMtkMumaa/6gm2++xZMzuXOg1NTdMplMnvzGbDarb9+BMgxD2dnZ2rs3P+dITLxYgwcP1YcffqD//OffatiwkSZNKvnfrEBzOffxzp49o02b8vc9ZcpTWrXqY6WlpRQ7D0Xbub8fOHCIkpMDj89XjJWdVwe6/6rM7wt+vgqe76pWU68HAaAymQzDMIIdRHkYhqGPPvpIS5cu9byPqlWrVho0aJDGjBnjeReV2+eff64FCxZoz549ysvLU7NmzdSnTx/deeedql27drH9b9++XXPmzNHOnTtlNpvVunVrDR8+XEOHDpXVWnlzfefPO5SZea7S9h8MDRvmP4WWnn42yJEAKAvGMFD9BWMcDx3627sFkpPXVNlxUTHq1ImU2WqW1WxVljNLyzOWKzUnVTnOHNksNiXaEjUkfkj+O6pcDjntzqBOVBX8vA396xsl1ERZnPr5R639x9Mym81avHix2rZt67Xed999p9GjR8vlcqn/oy+oXhPfTwugZMlPTfjta/4NrfZCPZ/mdzbgX6iPYwAlYwxXHfe5Lo1q/WSVJJlMJg0dOlRDhw4NqP4111yja665JuD99+jRQ2+//XZZwwMAAKjRoqKilZt7jvdkVVOZmflPUtWtG6UYa4xua3SbLCaLp9xpOGWWWXa7nXdU1QD1mrRQu9/foG//s0HTp0/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+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 285,
+       "width": 853
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plot_yhat_vs_y(df_h_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observe:**\n",
+    "\n",
+    "+ The predictions `yhat` look pretty close to the true value `y`, usually well-within the $HDI_{94}$\n",
+    "+ As we would expect, the distributions of `yhat` are useful too: quantifing the uncertainty in prediction and letting us\n",
+    "  make better decisions accordingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Errata\n",
+    "\n",
+    "## Authors\n",
+    "\n",
+    "+ Created by [Jonathan Sedar](https://github.com/jonsedar) in Nov 2024\n",
+    "\n",
+    "## Reference\n",
+    "\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Mon Dec 16 2024\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.11.10\n",
+      "IPython version      : 8.29.0\n",
+      "\n",
+      "pymc      : 5.16.2\n",
+      "matplotlib: 3.9.2\n",
+      "pandas    : 2.2.3\n",
+      "pytensor  : 2.25.5\n",
+      "arviz     : 0.20.0\n",
+      "seaborn   : 0.12.2\n",
+      "numpy     : 1.26.4\n",
+      "\n",
+      "Watermark: 2.5.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tested running on Google Colab 2024-12-16\n",
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "oreum_survival",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "widgets": {
+   "state": {
+    "2478528dd27c4eed9219f055bf907997": {
+     "views": [
+      {
+       "cell_index": 67
+      }
+     ]
+    },
+    "c789864f037443ada1d6ad40c44524f4": {
+     "views": [
+      {
+       "cell_index": 68
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/generalized_linear_models/GLM-missing-values-in-covariates.myst.md b/examples/generalized_linear_models/GLM-missing-values-in-covariates.myst.md
new file mode 100644
index 00000000..dfcfa84b
--- /dev/null
+++ b/examples/generalized_linear_models/GLM-missing-values-in-covariates.myst.md
@@ -0,0 +1,1586 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: oreum_survival
+  language: python
+  name: python3
+---
+
+(GLM-missing-values-in-covariates.ipynb)=
+# GLM-missing-values-in-covariates
+
+:::{post} Nov 09, 2024
+:tags: missing-covariate-values, missing-values, auto-imputation, linear-regression, bayesian-workflow
+:category: intermediate, reference
+:author: Jonathan Sedar
+:::
+
+Minimal Reproducible Example: Workflow to handle missing data in multiple covariates (numeric predictor features)
+
+
+## Automatic Imputation of Missing Values In Covariates: Worked Example with Bayesian Workflow
+
+Here we demonstrate **automatic imputation of missing values within multiple covariates (numeric predictor features)**:
+
+```
+y ~ x + e
+y: Numeric target
+x: Numeric with missing values in covariates (numeric predictor features)
+```
+
+**Disclaimer:**
++ This Notebook is a worked example only, it's not intended to be an academic reference
++ The theory and math may be incorrect, incorrectly notated, or incorrectly used
++ The code may contain errors, inefficiencies, legacy methods, and the text may have typos
++ Use at your own risk!
+
+## Contents
+
++ [Discussion](#Discussion)
+
++ [Setup](#Setup)
+
++ [0. Curate Dataset](#0.-Curate-Dataset)
+
++ [1. `Model0`: Baseline without Missing Values](#1.-`Model0`:-Baseline-without-Missing-Values)
+
++ [2. `ModelA`: Auto-impute Missing Values](#2.-`ModelA`:-Auto-impute-Missing-Values)
+
+---
+
+---
+
++++
+
+# Discussion
+
++++
+
+## Problem Statement
+
+We often encounter real-world situations and datasets where a predictor feature is numeric and where observations contain
+missing values in that feature. 
+
+Missing values break the model inference, because the log-likelihood for those observations can't be computed.
+
+We have a few options to mitigate the situation:
+
++ Firstly, we should always try to learn _why_ the values are missing. If they are _not_ Missing Completely At Random 
+  (MCAR) {cite:p}`enders2022` and contain latent information about a biased or lossy data-generating process, then we 
+  might choose to **remove the observations with missing vales** or **ignore the features that contain missing values**
++ If we believe the values **are** Missing Completely At Random (MCAR), then we might choose to 
+  **auto-impute the missing values** so that we can make use of all the available observations. This is particularly 
+  acute when we have few observations and/or a high-degree of missingness.
+
+Here we demonstrate method(s) to auto-impute missing values as part of the `pymc` posterior sampling process. We get
+inference and prediction as usual, but also auto-imputed values for the missing values. Pretty neat!
+
+
+## Data & Models Demonstrated
+
+Our problem statement is that when faced with data with missing values, we want to:
+
+1. **Infer** the missing values for the in-sample dataset and sample full posterior parameters
+2. **Predict** the endogenous feature and the missing values for an out-of-sample dataset
+    
+    
+This notebook takes the opportunity to:
+
++ Demonstrate a general method using auto-imputation, which is often mentioned in `pymc` folklore but rarely
+  shown in full. A helpful primer and related discussion is this PyMCon2020 talk: {cite:p}`junpenglao2020`
++ Demonstrate a reasonably complete Bayesian workflow {cite:p}`gelman2020bayesian` including data creation
+
+This notebook is a partner to another `pymc-examples` notebook **Missing_Data_Imputation.ipynb**
+which goes into more detail of taxonomies and a much more complicated dataset and tutorial-style worked example.
+
++++
+
+---
+
+---
+
++++
+
+# Setup
+
++++
+
+:::{include} ../extra_installs.md
+:::
+
+```{code-cell} ipython3
+# uncomment to install in a Google Colab environment
+# !pip install watermark
+```
+
+```{code-cell} ipython3
+---
+jupyter:
+  outputs_hidden: false
+slideshow:
+  slide_type: skip
+---
+# suppress seaborn, it's far too chatty
+import warnings  # #noqa
+
+from copy import deepcopy
+
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pymc as pm
+import pytensor.tensor as pt
+
+from pymc.testing import assert_no_rvs
+
+warnings.simplefilter(action="ignore", category=FutureWarning)  # noqa
+import seaborn as sns
+```
+
+```{code-cell} ipython3
+%matplotlib inline
+%config InlineBackend.figure_format = 'retina'
+
+sns.set_theme(
+    style="darkgrid",
+    palette="muted",
+    context="notebook",
+    rc={"figure.dpi": 72, "savefig.dpi": 144, "figure.figsize": (12, 4)},
+)
+
+SAMPLE_KWS = dict(
+    progressbar=True,
+    draws=500,
+    tune=3000,  # tune a little more than usual
+    chains=4,
+    idata_kwargs=dict(log_likelihood=True),
+)
+
+RNG = np.random.default_rng(seed=42)
+KWS_MN = dict(markerfacecolor="w", markeredgecolor="#333333", marker="d", markersize=12)
+KWS_BOX = dict(kind="box", showmeans=True, whis=(3, 97), meanprops=KWS_MN)
+KWS_PNT = dict(estimator=np.mean, errorbar=("ci", 94), join=False, color="#32CD32")
+KWS_SCTR = dict(s=80, color="#32CD32")
+```
+
+---
+
+---
+
++++
+
+# 0. Curate Dataset
+
++++
+
+**IMPLEMENTATION NOTE**
+
++ The auto-imputation and full workflow will be simplest to demonstrate if we
+  can compare to true values for the missing data, which means we have to
+  synthesize the dataset
++ We will create create at least 2 features that contain missing values, in
+  order to demonstrate the flattening effect of `pymc`'s auto-imputation routine
++ We create missing values that are Missing At Random (MAR) {cite:p}`enders2022`
++ We also take the opportunity to make the missingness a real problem, with a
+  large proportion of values missing (`40%`)
++ For simplicity, we will assume the original features are each Normally
+  distributed: to make this example harder, the reader could experiment with
+  other distributions
+
++++
+
+## 0.1 Create Synthetic Dataset
+
++++
+
+### 0.1.0 Create "true" (unobserved) dataset (without missing values)
+
+```{code-cell} ipython3
+REFVAL_X_MU = dict(a=1, b=1, c=10, d=2)
+REFVAL_X_SIGMA = dict(a=1, b=4, c=1, d=10)
+REFVAL_BETA = dict(intercept=-4, a=10, b=-20, c=30, d=5)
+```
+
+```{code-cell} ipython3
+N = 40
+dfraw = pd.DataFrame(
+    {
+        "a": RNG.normal(loc=REFVAL_X_MU["a"], scale=REFVAL_X_SIGMA["a"], size=N),
+        "b": RNG.normal(loc=REFVAL_X_MU["b"], scale=REFVAL_X_SIGMA["b"], size=N),
+        "c": RNG.normal(loc=REFVAL_X_MU["c"], scale=REFVAL_X_SIGMA["c"], size=N),
+        "d": RNG.normal(loc=REFVAL_X_MU["d"], scale=REFVAL_X_SIGMA["d"], size=N),
+    },
+    index=[f"o{str(i).zfill(2)}" for i in range(N)],
+)
+dfraw.index.name = "oid"
+dfraw["y"] = (
+    REFVAL_BETA["intercept"]
+    + (dfraw * np.array(list(REFVAL_BETA.values()))[1:]).sum(axis=1)
+    + RNG.normal(loc=0, scale=1, size=N)
+)
+dfraw.tail()
+```
+
+### 0.1.1 Force 2 features to contain 40% unobserved missing values
+
+```{code-cell} ipython3
+df = dfraw.copy()
+
+prop_missing = 0.4
+df.loc[RNG.choice(df.index, int(N * prop_missing), replace=False), "c"] = np.nan
+df.loc[RNG.choice(df.index, int(N * prop_missing), replace=False), "d"] = np.nan
+idx = df[["c", "d"]].isnull().sum(axis=1) > 0
+display(df.loc[idx])
+display(pd.concat((df.describe(include="all").T, df.isnull().sum(), df.dtypes), axis=1))
+```
+
+**Observe:**
+
++ Features `a` and `b` are full, complete, without missing values
++ Features `c` and `d` contain missing values, where observations can even contain missing values for both features
+
+> NOTE we dont need any further steps to prepare the dataset (e.g. clean observations & features, force datatypes, set 
+> indexes, etc), so we will move straight to EDA and transformations for model input
+
++++
+
+## 0.2 Very limited quick EDA
+
++++
+
+### 0.2.1 Univariate: target `y`
+
+```{code-cell} ipython3
+def plot_univariate_violin(df: pd.DataFrame, fts: list):
+    v_kws = dict(data=df, cut=0)
+    cs = sns.color_palette(n_colors=len(fts))
+    height_bump = 2 if len(fts) == 1 else 1
+    f, axs = plt.subplots(
+        len(fts), 1, figsize=(12, 0.8 + height_bump * 1.2 * len(fts)), squeeze=False, sharex=True
+    )
+    for i, ft in enumerate(fts):
+        ax = sns.violinplot(x=ft, **v_kws, ax=axs[i][0], color=cs[i])
+        n_nans = pd.isnull(df[ft]).sum()
+        _ = ax.text(
+            0.993,
+            0.93,
+            f"NaNs: {n_nans}",
+            transform=ax.transAxes,
+            ha="right",
+            va="top",
+            backgroundcolor="w",
+            fontsize=10,
+        )
+        _ = ax.set_title(f"ft: {ft}")
+    _ = f.suptitle("Univariate numerics with NaN count noted")
+    _ = f.tight_layout()
+
+
+plot_univariate_violin(df, fts=["y"])
+```
+
+**Observe:**
+
++ `y` Looks smooth, reasonably central, can probably model with a Normal likelihood
+
++++
+
+### 0.2.2 Univariate: predictors `a, b, c, d`
+
+```{code-cell} ipython3
+plot_univariate_violin(df, fts=["a", "b", "c", "d"])
+```
+
+**Observe:**
+
++ `a`, `b`, `c`, `d` Vary across the range, but all reasonably central, can probably model with Normal distributions
++ `c`, `d` each contain `16` NaN values
+
++++
+
+### 0.2.3 Bivariate: target vs predictors
+
+```{code-cell} ipython3
+dfp = df.reset_index().melt(id_vars=["oid", "y"], var_name="ft")
+g = sns.lmplot(
+    y="y",
+    x="value",
+    hue="ft",
+    col="ft",
+    data=dfp,
+    fit_reg=True,
+    height=4,
+    aspect=0.75,
+    facet_kws={"sharex": False},
+)
+_ = g.fig.suptitle("Bivariate plots of `y` vs fts `a`, `b`, `c`, `d`")
+_ = g.fig.tight_layout()
+```
+
+**Observe:**
+
++ Each of features `a`, `b`, `c`, `d` has a correlation to `y`, some stronger, some weaker
++ This looks fairly realistic
+
++++
+
+## 0.3 Transform dataset to `dfx` for model input
+
++++
+
+**IMPORTANT NOTE**
+
++ Reminder that Bayesian inferential methods **do not need** a `test` dataset (nor k-fold cross validation)
+  to fit parameters. 
++ We also **do not need** a `holdout` (out-of-sample) dataset (that contains target `y` values) to evaluate model
+  performance, because we can use in-sample PPC, LOO-PIT and ELPD evaluations
++ Depending on the real-world model implementation we might:
+  + Create a separate `holdout` set (even though we dont need it) to simply eyeball the behaviour of predictive outputs
+  + Create a `forecast` set (which does not have target `y` values) to demonstrate how we would use the model and it's
+    predictive outputs on future new data in Production
++ Here we **do** take a `holdout` set, in order to eyeball the predictive outputs, and also to eyeball the auto-imputed
+  missing values compared to the true synthetic data. This is only possible because we synthesized the true data above.
++ Per the following terminology, `df` created above is our "working" dataset, which we will partition into "train" and 
+  "holdout"
+
+**Dataset terminology / partitioning / purpose:**
+
+```text
+|<---------- Relevant domain of all data for our analyses & models ---------->|
+|<----- "Observed" historical target ------>||<- "Unobserved" future target ->|
+|<----------- "Working" dataset ----------->||<----- "Forecast" dataset ----->|
+|<- Training/CrossVal ->||<- Test/Holdout ->|
+```
+
++++
+
+### 0.3.1 Separate `df_train` and `df_holdout`
+
++++
+
+**NOTE**
+
++ We have full control over how many observations we create in `df`, so the ratio of this split doesn't really matter,
+  and we'll arrange to have `10` observations in the holdout set
++ Eyeball the `count` of non-nulls in the below tabels to ensure we have missing values in both features `c`, `d` in 
+  both datasets `train` and `holdout`
+
+```{code-cell} ipython3
+df_train = df.sample(n=len(df) - 10, replace=False).sort_index()
+print(df_train.shape)
+display(
+    pd.concat(
+        (df_train.describe(include="all").T, df_train.isnull().sum(), df_train.dtypes), axis=1
+    )
+)
+```
+
+```{code-cell} ipython3
+df_holdout = df.loc[list(set(df.index.values) - set(df_train.index.values))].copy().sort_index()
+print(df_holdout.shape)
+display(
+    pd.concat(
+        (df_holdout.describe(include="all").T, df_holdout.isnull().sum(), df_holdout.dtypes), axis=1
+    )
+)
+```
+
+### 0.3.2 Create `dfx_train`
+
++++
+
+Transform (zscore and scale) numerics
+
+```{code-cell} ipython3
+FTS_NUM = ["a", "b", "c", "d"]
+FTS_NON_NUM = []
+FTS_Y = ["y"]
+MNS = np.nanmean(df_train[FTS_NUM], axis=0)
+SDEVS = np.nanstd(df_train[FTS_NUM], axis=0)
+
+dfx_train_num = (df_train[FTS_NUM] - MNS) / SDEVS
+icpt = pd.Series(np.ones(len(df_train)), name="intercept", index=dfx_train_num.index)
+
+# concat including y which will be used as observed
+dfx_train = pd.concat((df_train[FTS_Y], icpt, df_train[FTS_NON_NUM], dfx_train_num), axis=1)
+display(dfx_train.describe().T)
+```
+
+### 0.3.3 Create `dfx_holdout`
+
+```{code-cell} ipython3
+dfx_holdout_num = (df_holdout[FTS_NUM] - MNS) / SDEVS
+icpt = pd.Series(np.ones(len(df_holdout)), name="intercept", index=dfx_holdout_num.index)
+
+# concat including y which will be used as observed
+dfx_holdout = pd.concat((df_holdout[FTS_Y], icpt, df_holdout[FTS_NON_NUM], dfx_holdout_num), axis=1)
+display(dfx_holdout.describe().T)
+```
+
+---
+
+---
+
++++
+
+# 1. `Model0`: Baseline without Missing Values
+
++++
+
+This section might seem unusual or unnecessary, but will hopefully provide a useful comparison for general behaviour
+and help to further explain the model architecture used in `ModelA`. 
+
+We will create `Model0` using the same general linear model, operating on the `dfrawx_train` dataset _without_ any
+missing values:
+
+$$
+\begin{align}
+\sigma_{\beta} &\sim \text{InverseGamma}(11, 10)  \\
+\beta_{j} &\sim \text{Normal}(0, \sigma_{\beta}, \text{shape}=j)  \\
+\\
+\epsilon &\sim \text{InverseGamma}(11, 10)  \\
+\hat{y_{i}} &\sim \text{Normal}(\mu=\beta_{j}^{T}\mathbb{x}_{ij}, \sigma=\epsilon)  \\
+\end{align}
+$$
+
+where:
++ Observations $i$ (`observation_id` aka `oid`) contain numeric features $j$ that have complete values (this is all features `a`, `b`, `c`, `d`)
++ Our target is $\hat{y_{i}}$, here of `y` with linear sub-model $\beta_{j}^{T}\mathbb{x}_{ij}$ 
+  to regress onto those features
+
++++
+
+## 1.0 Quickly prepare non-missing datasets based on `dfraw`
+
++++
+
+This is a lightly simplifed copy of the same logic / workflow in $\S0.3$ above. We won't take up any more space here 
+with EDA, the only difference is `c` and `d` are now complete
+
++++
+
+Partition `dfraw` into `dfraw_train` and `dfraw_holdout`, use same indexes as `df_train` and `df_holdout`
+
+```{code-cell} ipython3
+dfraw_train = dfraw.loc[df_train.index].copy()
+dfraw_holdout = dfraw.loc[df_holdout.index].copy()
+dfraw_holdout.tail()
+```
+
+Create `dfrawx_train`: Transform (zscore and scale) numerics
+
+```{code-cell} ipython3
+MNS_RAW = np.nanmean(dfraw_train[FTS_NUM], axis=0)
+SDEVS_RAW = np.nanstd(dfraw_train[FTS_NUM], axis=0)
+
+dfrawx_train_num = (dfraw_train[FTS_NUM] - MNS_RAW) / SDEVS_RAW
+icpt = pd.Series(np.ones(len(dfraw_train)), name="intercept", index=dfrawx_train_num.index)
+
+# concat including y which will be used as observed
+dfrawx_train = pd.concat(
+    (dfraw_train[FTS_Y], icpt, dfraw_train[FTS_NON_NUM], dfrawx_train_num), axis=1
+)
+display(dfrawx_train.describe().T)
+```
+
+Create `dfrawx_holdout`
+
+```{code-cell} ipython3
+dfrawx_holdout_num = (dfraw_holdout[FTS_NUM] - MNS_RAW) / SDEVS_RAW
+icpt = pd.Series(np.ones(len(dfraw_holdout)), name="intercept", index=dfrawx_holdout_num.index)
+
+# concat including y which will be used as observed
+dfrawx_holdout = pd.concat(
+    (dfraw_holdout[FTS_Y], icpt, dfraw_holdout[FTS_NON_NUM], dfrawx_holdout_num), axis=1
+)
+display(dfrawx_holdout.describe().T)
+```
+
+Note the inevitable (but slight) difference in `MNS` vs `MNS_RAW` and `SDEVS` vs `SDEVS_RAW`
+
+```{code-cell} ipython3
+print(MNS)
+print(MNS_RAW)
+print(SDEVS)
+print(SDEVS_RAW)
+```
+
+## 1.1 Build Model Object
+
+```{code-cell} ipython3
+ft_y = "y"
+FTS_XJ = ["intercept", "a", "b", "c", "d"]
+
+COORDS = dict(
+    xj_nm=FTS_XJ,  # these are the names of the features
+    oid=dfrawx_train.index.values,  # these are the observation_ids
+)
+
+with pm.Model(coords=COORDS) as mdl0:
+    # 0. create (Mutable)Data containers for obs (Y, X)
+    y = pm.Data("y", dfrawx_train[ft_y].values, dims="oid")
+    xj = pm.Data("xj", dfrawx_train[FTS_XJ].values, dims=("oid", "xj_nm"))
+
+    # 2. define priors for contiguous data
+    b_s = pm.Gamma("beta_sigma", alpha=10, beta=10)  # E ~ 1
+    bj = pm.Normal("beta_j", mu=0, sigma=b_s, dims="xj_nm")
+
+    # 4. define evidence
+    epsilon = pm.Gamma("epsilon", alpha=50, beta=50)  # encourage E ~ 1
+    lm = pt.dot(xj, bj.T)
+    _ = pm.Normal("yhat", mu=lm, sigma=epsilon, observed=y, dims="oid")
+
+RVS_PPC = ["yhat"]
+RVS_PRIOR = ["epsilon", "beta_sigma", "beta_j"]
+```
+
+##### Verify the built model structure matches our intent, and validate the parameterization
+
+```{code-cell} ipython3
+display(pm.model_to_graphviz(mdl0, formatting="plain"))
+display(dict(unobserved=mdl0.unobserved_RVs, observed=mdl0.observed_RVs))
+assert_no_rvs(mdl0.logp())
+mdl0.debug(fn="logp", verbose=True)
+mdl0.debug(fn="random", verbose=True)
+```
+
+**Observe:**
+
++ This is a very straightforward model
+
++++
+
+## 1.2 Sample Prior Predictive, View Diagnostics
+
+```{code-cell} ipython3
+GRP = "prior"
+kws = dict(samples=2000, return_inferencedata=True, random_seed=42)
+with mdl0:
+    id0 = pm.sample_prior_predictive(var_names=RVS_PPC + RVS_PRIOR, **kws)
+```
+
+### 1.2.1 In-Sample Prior PPC (Retrodictive Check)
+
+```{code-cell} ipython3
+def plot_ppc_retrodictive(
+    idata: az.InferenceData,
+    grp: str = "posterior",
+    rvs: list = None,
+    mdlnm: str = "mdla",
+    ynm: str = "y",
+) -> plt.Figure:
+    """Convenience plot prior or posterior PPC retrodictive KDE"""
+    f, axs = plt.subplots(1, 1, figsize=(12, 3))
+    _ = az.plot_ppc(idata, group=grp, kind="kde", var_names=rvs, ax=axs, observed=True)
+    _ = f.suptitle(f"In-sample {grp.title()} PPC Retrodictive KDE on `{ynm}` - `{mdlnm}`")
+    return f
+
+
+f = plot_ppc_retrodictive(id0, grp=GRP, rvs=["yhat"], mdlnm="mdl0", ynm="y")
+```
+
+**Observe:**
+
++ The prior PPC is wrong as expected, because we've set relatively uninformative priors
++ However the general range and scale is reasonable and the sampler should be able to 
+  find the highest likelihood latent space easily
+
++++
+
+### 1.2.2 Quick look at selected priors
+
++++
+
+#### Coefficients etc
+
+```{code-cell} ipython3
+def plot_krushke(
+    idata: az.InferenceData,
+    group: str = "posterior",
+    rvs: list = RVS_PRIOR,
+    coords: dict = None,
+    ref_vals: list = None,
+    mdlnm: str = "mdla",
+    n: int = 1,
+    nrows: int = 1,
+) -> plt.figure:
+    """Convenience plot Krushke-style posterior (or prior) KDE"""
+    m = int(np.ceil(n / nrows))
+    f, axs = plt.subplots(nrows, m, figsize=(3 * m, 0.8 + nrows * 2))
+    _ = az.plot_posterior(
+        idata, group=group, ax=axs, var_names=rvs, coords=coords, ref_val=ref_vals
+    )
+    _ = f.suptitle(f"{group.title()} distributions for rvs {rvs} - `{mdlnm}")
+    _ = f.tight_layout()
+    return f
+
+
+f = plot_krushke(id0, GRP, rvs=RVS_PRIOR, mdlnm="mdl0", n=1 + 1 + 5, nrows=2)
+```
+
+**Observe:**
+
++ Model priors `beta_sigma`, `beta_j: (levels)`, `epsilon` all have reasonable prior ranges as specified
+
++++
+
+## 1.3 Sample Posterior, View Diagnostics
+
++++
+
+### 1.3.1 Sample Posterior and PPC
+
+```{code-cell} ipython3
+GRP = "posterior"
+with mdl0:
+    id0.extend(pm.sample(**SAMPLE_KWS), join="right")
+    id0.extend(
+        pm.sample_posterior_predictive(trace=id0.posterior, var_names=RVS_PPC),
+        join="right",
+    )
+```
+
+### 1.3.2 View Traces
+
+```{code-cell} ipython3
+def plot_traces_and_display_summary(
+    idata: az.InferenceData, rvs: list = None, coords: dict = None, mdlnm="mdla", energy=False
+) -> plt.Figure:
+    """Convenience to plot traces and display summary table for rvs"""
+    _ = az.plot_trace(idata, var_names=rvs, coords=coords, figsize=(12, 0.8 + 1.1 * len(rvs)))
+    f = plt.gcf()
+    _ = f.suptitle(f"Posterior traces of {rvs} - `{mdlnm}`")
+    _ = f.tight_layout()
+    if energy:
+        _ = az.plot_energy(idata, fill_alpha=(0.8, 0.6), fill_color=("C0", "C8"), figsize=(12, 1.6))
+    display(az.summary(idata, var_names=rvs))
+    return f
+
+
+f = plot_traces_and_display_summary(id0, rvs=RVS_PRIOR, mdlnm="mdl0", energy=True)
+```
+
+**Observe:**
+
++ Samples well-mixed and well-behaved: `ess_bulk` is good, `r_hat` is good
++ Marginal energy | energy transition looks a little mismatched, but `E-BFMI >> 0.3` so is
+  [apparently reasonable](https://python.arviz.org/en/stable/api/generated/arviz.bfmi.html#arviz.bfmi)
+
++++
+
+### 1.3.3 In-Sample Posterior PPC (Retrodictive Check)
+
+```{code-cell} ipython3
+f = plot_ppc_retrodictive(id0, grp=GRP, rvs=["yhat"], mdlnm="mdl0", ynm="y")
+```
+
+**Observe:**
+
++ In-sample PPC `yhat` tracks the observed `y` very closely
+
++++
+
+### 1.3.4 In-Sample PPC LOO-PIT
+
+```{code-cell} ipython3
+def plot_loo_pit(
+    idata: az.InferenceData, mdlname: str = "mdla", y: str = "yhat", y_hat: str = "yhat"
+) -> plt.Figure:
+    """Convenience plot LOO-PIT KDE and ECDF"""
+    f, axs = plt.subplots(1, 2, figsize=(12, 2.4))
+    _ = az.plot_loo_pit(idata, y=y, y_hat=y_hat, ax=axs[0])
+    _ = az.plot_loo_pit(idata, y=y, y_hat=y_hat, ax=axs[1], ecdf=True)
+    _ = axs[0].set_title(f"Predicted `{y_hat}` LOO-PIT")
+    _ = axs[1].set_title(f"Predicted `{y_hat}` LOO-PIT cumulative")
+    _ = f.suptitle(f"In-sample LOO-PIT `{mdlname}`")
+    _ = f.tight_layout()
+    return f
+
+
+f = plot_loo_pit(id0, "mdla")
+```
+
+**Observe:**
+
++ `LOO-PIT` looks good, very slightly overdispersed but more than acceptable for use
+
++++
+
+## 1.4 Evaluate Posterior Parameters
+
++++
+
+### 1.4.1 Coefficients etc
+
+```{code-cell} ipython3
+f = plot_krushke(id0, GRP, RVS_PRIOR, mdlnm="mdl0", n=1 + 1 + 5, nrows=2)
+```
+
+**Observe:**
+
++ Posteriors for model coeffs `beta_sigma`, `beta_j: (levels)`, `epsilon` all smooth and central as specified
+
++++
+
+#### For interest's sake forestplot the `beta_j` levels to compare relative effects
+
+```{code-cell} ipython3
+def plot_forest(
+    idata: az.InferenceData, grp: str = "posterior", rvs: list = None, mdlnm="mdla"
+) -> plt.Figure:
+    """Convenience forestplot posterior (or prior) KDE"""
+
+    n = sum([idata[grp][rv].shape[-1] for rv in rvs])
+    f, axs = plt.subplots(1, 1, figsize=(12, 0.6 + 0.3 * n))
+    _ = az.plot_forest(idata[grp], var_names=rvs, ax=axs, combined=True)
+    _ = f.suptitle(f"Forestplot of {grp.title()} level values for `{rvs}` - `{mdlnm}`")
+    _ = f.tight_layout()
+    return f
+
+
+f = plot_forest(id0, grp=GRP, rvs=["beta_j"], mdlnm="mdl0")
+```
+
+**Observe:**
+
++ Very tight and distinct posterior distributions
++ The levels broadly correspond to what we would expect to see, given the synthetic data creation
+
++++
+
+## 1.5 Create PPC Forecast on `dfrawx_holdout` set
+
++++
+
+### 1.5.1 Replace dataset with `dfrawx_holdout`
+
+```{code-cell} ipython3
+COORDS_F = deepcopy(COORDS)
+COORDS_F["oid"] = dfrawx_holdout.index.values
+mdl0.set_data("xj", dfrawx_holdout[FTS_XJ].values, coords=COORDS_F)
+```
+
+### 1.5.2 Sample PPC for `yhat`
+
+```{code-cell} ipython3
+with mdl0:
+    id0_h = pm.sample_posterior_predictive(trace=id0.posterior, var_names=RVS_PPC, predictions=True)
+```
+
+### 1.5.3 Out-of-sample: Compare forecasted `yhat` to known true value `y`
+
++++
+
+##### Extract `yhat` from PPC idata, and attach real values (only available because it's a `holdout` set)
+
+```{code-cell} ipython3
+dfraw_h_y = (
+    az.extract(id0_h, group="predictions", var_names=["yhat"])
+    .to_dataframe()
+    .drop(["chain", "draw"], axis=1)
+    .reset_index()
+    .set_index(["oid"])
+)
+dfraw_h_y = pd.merge(dfraw_h_y, dfraw_holdout[["y"]], how="left", left_index=True, right_index=True)
+dfraw_h_y.describe().T
+```
+
+##### Plot posterior `yhat` vs known true values `y` (only available because it's a `holdout` set)
+
+```{code-cell} ipython3
+def plot_yhat_vs_y(
+    df_h: pd.DataFrame, yhat: str = "yhat", y: str = "y", mdlnm: str = "mdla"
+) -> plt.Figure:
+    """Convenience plot forecast yhat with overplotted y from holdout set"""
+    g = sns.catplot(x=yhat, y="oid", data=df_h.reset_index(), **KWS_BOX, height=4, aspect=3)
+    _ = g.map(sns.scatterplot, y, "oid", **KWS_SCTR, zorder=100)
+    _ = g.fig.suptitle(
+        f"Out-of-sample: boxplots of posterior `{yhat}` with overplotted actual `{y}` values"
+        + f" per observation `oid` (green dots) - `{mdlnm}`"
+    )
+    _ = g.tight_layout()
+    return g.fig
+
+
+_ = plot_yhat_vs_y(dfraw_h_y, mdlnm="mdl0")
+```
+
+**Observe:**
+
++ The predictions `yhat` look very close to the true value `y`, usually well within the $HDI_{94}$ and $HDI_{50}$
++ As we would expect, the distributions of `yhat` are useful too: quantifing the uncertainty in prediction and letting us
+  make better decisions accordingly.
+
++++
+
+---
+
+---
+
++++
+
+# 2. `ModelA`: Auto-impute Missing Values
+
++++
+
+Now we progress to handling missing values!
+
+`ModelA` is an extension of `Model0` with a simple linear submodel with a hierarchical prior on the data for 
+features $k$ that have missing values:
+
+$$
+\begin{align}
+\sigma_{\beta} &\sim \text{InverseGamma}(11, 10)  \\
+\beta_{j} &\sim \text{Normal}(0, \sigma_{\beta}, \text{shape}=j)  \\
+\beta_{k} &\sim \text{Normal}(0, \sigma_{\beta}, \text{shape}=k)  \\
+\\
+\mu_{k} &\sim \text{Normal}(0, 1, \text{shape}=k)  \\
+\mathbb{x}_{ik} &\sim \text{Normal}(\mu_{k}, 1, \text{shape}=(i,k))  \\
+\\
+\epsilon &\sim \text{InverseGamma}(11, 10)  \\
+\hat{y_{i}} &\sim \text{Normal}(\mu=\beta_{j}^{T}\mathbb{x}_{ij} + \beta_{k}^{T}\mathbb{x}_{ik}, \sigma=\epsilon)  \\
+\end{align}
+$$
+
+where:
++ Observations $i$ contain numeric features $j$ that have complete values, and numeric features $k$ that contain 
+  missing values
++ For the purposes of this example, we assume that in future, features $j$ will always be complete, and missing values 
+  can occur in $k$, and design the model accordingly
++ This is a big assumption, because missing values could theoretically occur in any feature, but we extend the example 
+  to assume we always require that features $j$ are complete
++ We treat data $\mathbb{x}_{ik}$ as a random variable, and "observe" the non-missing values. 
++ We will assume the missing data values to have a Normal distribution with mean $\mu_{k}$ - this is reasonable because
+  we zscored the `dfx` data and can expect a degree of centrality around 0. Of course, the actual distributions of data
+  in $\mathbb{x}_{ik}$ could be highly skewed, so a Normal is not necessarily the best choice, but a good place to start
++ Our target is $\hat{y_{i}}$, here of `y` with linear sub-models $\beta_{j}^{T}\mathbb{x}_{ij}$ and 
+  $\beta_{k}^{T}\mathbb{x}_{ik}$ to regress onto those features
+
+
+**IMPLEMENTATION NOTE**
+
+There are a few ways to handle missing values in `pymc`. Here in `ModelA` we make use of `pymc` "auto-imputation",
+where we instruct the model to "observe" a slice of the dataset `dfx_train[FTS_XK]` for the features that contain 
+missing values , and the `pymc` build processes will very kindly handle the missing values and provide auto-imputation 
+at element level.
+
+This is convenient and clean in the model specification, but does require further manipulation of the model and idata
+to create out-of-sample predictions, see $\S 2.5$ for detail
+
+In particular:
++ The auto-imputation routine will create flattened `xk_observed` and `xk_unobserved` RVs for us, based on a (single)
+  specified distribution of `xk`, and a final `Deterministic` for `xk` with the correct dimensions
++ **However, an important limitation** is that currently, `pm.Data` **cannot** contain `NaNs` (nor a `masked_array`) so
+  we can't use the usual workflow `mdl.set_data()` to replace the in-sample dataset with an out-of-sample dataset
+  to make predictions!
++ For example, neither of these constructs is possible:
+
+  1. Cannot insert `NaNs` into `pm.Data`
+      ```python
+      xk_data = pm.Data('xk_data', dfx_train[FTS_XK].values, dims=('oid', 'xk_nm'))
+      xk_ma = np.ma.masked_array(xk_data, mask=np.isnan(xk_data.values))
+      ```
+  2. Cannot insert `masked_array` into `pm.Data`
+      ```python
+      xk_ma = pm.Data('xk_ma', np.ma.masked_array(dfx_train[FTS_XK].values, mask=np.isnan(dfx_train[FTS_XK].values)))
+      ```
++ Also see further discussion in [pymc issue #6626](https://github.com/pymc-devs/pymc/issues/6626), and 
+  [proposed new functionality #7204](https://github.com/pymc-devs/pymc/issues/7204)
++ Finally, note that some earlier examples of auto-imputation involve creating a `np.ma.masked_array` and passing that
+  into the observed RV. As at Dec 2024 this appears to no longer be necessary, and we can directly pass in the dataframe
+  
+  For example, this:
+    ```python
+    xk_ma = np.ma.masked_array(dfx_train[FTS_XK].values, mask=np.isnan(dfx_train[FTS_XK].values))
+    xk = pm.Normal("xk", mu=xk_mu, sigma=1.0, observed=xk_ma, dims=("oid", "xk_nm"))
+    ```
+
+  can now be simply stated as:
+    ```python
+    xk = pm.Normal("xk", mu=xk_mu, sigma=1.0, observed=dfx_train[FTS_XK].values, dims=("oid", "xk_nm"))
+    ```
+
+
++++
+
+## 2.1 Build Model Object
+
+```{code-cell} ipython3
+ft_y = "y"
+FTS_XJ = ["intercept", "a", "b"]
+FTS_XK = ["c", "d"]
+COORDS = dict(
+    xj_nm=FTS_XJ,  # names of the features j
+    xk_nm=FTS_XK,  # names of the features k
+    oid=dfx_train.index.values,  # these are the observation_ids
+)
+
+with pm.Model(coords=COORDS) as mdla:
+    # 0. create (Mutable)Data containers for obs (Y, X)
+    y = pm.Data("y", dfx_train[ft_y].values, dims="oid")
+    xj = pm.Data("xj", dfx_train[FTS_XJ].values, dims=("oid", "xj_nm"))
+
+    # 1. create auto-imputing likelihood for missing data values
+    # NOTE: there's no way to put a nan-containing array (nor a np.masked_array)
+    # into a pm.Data, so dfx_train[FTS_XK].values has to go in directly
+    xk_mu = pm.Normal("xk_mu", mu=0.0, sigma=1, dims="xk_nm")
+    xk = pm.Normal(
+        "xk", mu=xk_mu, sigma=1.0, observed=dfx_train[FTS_XK].values, dims=("oid", "xk_nm")
+    )
+
+    # 2. define priors for contiguous and auto-imputed data
+    b_s = pm.Gamma("beta_sigma", alpha=10, beta=10)  # E ~ 1
+    bj = pm.Normal("beta_j", mu=0, sigma=b_s, dims="xj_nm")
+    bk = pm.Normal("beta_k", mu=0, sigma=b_s, dims="xk_nm")
+
+    # 4. define evidence
+    epsilon = pm.Gamma("epsilon", alpha=50, beta=50)  # encourage E ~ 1
+    lm = pt.dot(xj, bj.T) + pt.dot(xk, bk.T)
+    _ = pm.Normal("yhat", mu=lm, sigma=epsilon, observed=y, dims="oid")
+
+RVS_PPC = ["yhat"]
+RVS_PRIOR = ["epsilon", "beta_sigma", "beta_j", "beta_k"]
+RVS_K = ["xk_mu"]
+RVS_XK = ["xk"]
+RVS_XK_UNOBS = ["xk_unobserved"]
+```
+
+##### Verify the built model structure matches our intent, and validate the parameterization
+
+```{code-cell} ipython3
+display(pm.model_to_graphviz(mdla, formatting="plain"))
+display(dict(unobserved=mdla.unobserved_RVs, observed=mdla.observed_RVs))
+assert_no_rvs(mdla.logp())
+mdla.debug(fn="logp", verbose=True)
+mdla.debug(fn="random", verbose=True)
+```
+
+**Observe:**
+
++ We have two new auto-created nodes `xk_observed` (observed data) and `xk_unobserved` (unobserved free RV), which 
+  each have same distribution as we specified for `xk`
++ The original `xk` is now represented by a new `Deterministic` node with a function of the two (concatenation and reshaping)
++ In particular note `xk_unobserved` has a new, flattened shape with length equal to the count of NaNs in the relevant 
+  features $k$, and it loses the `dims` that we assigned to `xk`. This is an unhelpful current limitation and means we
+  have to do some indexing to recover the PPC values later
+
++++
+
+## 2.2 Sample Prior Predictive, View Diagnostics
+
+```{code-cell} ipython3
+GRP = "prior"
+kws = dict(samples=2000, return_inferencedata=True, random_seed=42)
+with mdla:
+    ida = pm.sample_prior_predictive(
+        var_names=RVS_PPC + RVS_PRIOR + RVS_K + RVS_XK + RVS_XK_UNOBS, **kws
+    )
+```
+
+### 2.2.1 In-Sample Prior PPC (Retrodictive Check)
+
+```{code-cell} ipython3
+f = plot_ppc_retrodictive(ida, grp=GRP, rvs=["yhat"], mdlnm="mdla", ynm="y")
+```
+
+**Observe:**
+
++ Values are wrong as expected, but range is reasonable
+
++++
+
+### 2.2.2 Quick look at selected priors
+
++++
+
+#### Coefficients etc
+
+```{code-cell} ipython3
+f = plot_krushke(ida, GRP, rvs=RVS_PRIOR, mdlnm="mdla", n=1 + 1 + 3 + 2, nrows=2)
+```
+
+**Observe:**
+
++ Model priors `beta_sigma`, `beta_j: (levels)`, `beta_k: (levels)`, `epsilon` all have reasonable prior ranges as specified
+
++++
+
+#### Hierarchical values $\mu_{k}$ for missing data in $\mathbb{x}_{k}$
+
+```{code-cell} ipython3
+f = plot_krushke(ida, GRP, RVS_K, mdlnm="mdla", n=2, nrows=1)
+```
+
+**Observe:**
+
++ Data imputation hierarchical priors `xk_mu (levels)` have reasonable prior ranges as specified
+
++++
+
+#### The values of missing data in $x_{k}$ (`xk_unobserved`)
+
+```{code-cell} ipython3
+f = plot_forest(ida, GRP, RVS_XK_UNOBS, "mdla")
+```
+
+**Observe:**
+
++ Prior values for the auto-imputed data `xk_unobserved` are of course all the same, and in reasonable ranges as specified
++ Note again that this is a flattened RV with length equal to the count of NaNs in features `c` and `d`
+
++++
+
+## 2.3 Sample Posterior, View Diagnostics
+
++++
+
+### 2.3.1 Sample Posterior and PPC
+
+```{code-cell} ipython3
+GRP = "posterior"
+with mdla:
+    ida.extend(pm.sample(**SAMPLE_KWS), join="right")
+    ida.extend(
+        pm.sample_posterior_predictive(trace=ida.posterior, var_names=RVS_PPC),
+        join="right",
+    )
+```
+
+### 2.3.2 View Traces
+
+```{code-cell} ipython3
+f = plot_traces_and_display_summary(ida, rvs=RVS_PRIOR + RVS_K, mdlnm="mdla", energy=True)
+```
+
+**Observe:**
+
++ Samples well-mixed and well-behaved: `ess_bulk` is good, `r_hat` is good
++ Marginal energy | energy transition looks a little mismatched: whilst `E-BFMI >> 0.3` (and is
+  [apparently reasonable](https://python.arviz.org/en/stable/api/generated/arviz.bfmi.html#arviz.bfmi)), note the
+  values are lower than for `Model0`. This is an effect of the missing data.
+
++++
+
+### 2.3.3 In-Sample Posterior PPC (Retrodictive Check)
+
+```{code-cell} ipython3
+f = plot_ppc_retrodictive(ida, grp=GRP, rvs=["yhat"], mdlnm="mdla", ynm="y")
+```
+
+**Observe:**
+
++ In-sample PPC `yhat` tracks the observed `y` moderately well: slightly overdispersed, perhaps a likelihood
+  with fatter tails would be more appropriate (e.g. StudentT)
+
++++
+
+### 2.3.4 In-Sample PPC LOO-PIT
+
+```{code-cell} ipython3
+f = plot_loo_pit(ida, "mdla")
+```
+
+**Observe:**
+
++ `LOO-PIT` looks good, again slightly overdispersed but acceptable for use
+
++++
+
+### 2.3.5 Compare Log-Likelihood vs Other Models
+
+```{code-cell} ipython3
+def plot_compare_log_likelihood(idatad: dict, yhat: str = "yhat") -> plt.Figure:
+    """Convenience to plot comparison for a dict of idatas"""
+    dfcomp = az.compare(idatad, var_name=yhat, ic="loo", method="stacking", scale="log")
+    f, axs = plt.subplots(1, 1, figsize=(12, 2 + 0.3 * len(idatad)))
+    _ = az.plot_compare(dfcomp, ax=axs, title=False, textsize=10, legend=False)
+    _ = f.suptitle(
+        "Model Performance Comparison: ELPD via In-Sample LOO-PIT: `"
+        + "` vs `".join(list(idatad.keys()))
+        + "`\n(higher & narrower is better)"
+    )
+    _ = f.tight_layout()
+    display(dfcomp)
+    return f
+
+
+f = plot_compare_log_likelihood(idatad={"mdl0": id0, "mdla": ida})
+```
+
+**Observe:**
+
++ Very interesting: our auto-imputing `ModelA` does of course suffer in comparison to `Model0` which has the benefit 
+  of the complete dataset (no missing values), but it's not that much worse, and (of course) we've been able to handle
+  missing values in the in-sample data!
+
++++
+
+## 2.4 Evaluate Posterior Parameters
+
++++
+
+### 2.4.1 Coefficients etc
+
+```{code-cell} ipython3
+f = plot_krushke(ida, GRP, RVS_PRIOR, mdlnm="mdla", n=1 + 1 + 3 + 2, nrows=2)
+```
+
+**Observe:**
+
++ Model coeffs `beta_sigma`, `beta_j: (levels)`, `beta_k: (levels)`, `epsilon` all have reasonable prior ranges as specified
+
++++
+
+#### For interest's sake forestplot the `beta_j` and `beta_k` levels to compare relative effects
+
+```{code-cell} ipython3
+f = plot_forest(ida, grp=GRP, rvs=["beta_j", "beta_k"], mdlnm="mdla")
+```
+
+**Observe:**
+
++ Very tight and distinct posterior distributions
++ Loosely compare this to the same plot for `Model0` in $\S1.4$ above: 
+
++++
+
+### 2.4.2 Hierarchical values for missing data in $\mathbb{x}_{k}$
+
+```{code-cell} ipython3
+f = plot_krushke(ida, GRP, RVS_K, mdlnm="mdla", n=2, nrows=1)
+```
+
+**Observe:**
+
++ Data imputation hierarchical priors haven't moved far from `0`, which is reasonable because the missingness is at random
++ However, they do show slight differences which is encouraging of them picking up the inherent differences in the 
+  primary raw data due to `REFVALS_X_MU`
+
++++
+
+### 2.4.3 View auto-imputed values of missing data in $x_{k}$ (`xk_unobserved`)
+
+```{code-cell} ipython3
+f = plot_forest(ida, GRP, RVS_XK_UNOBS, "mdla")
+```
+
+**Observe:**
+
++ We have used our model to autoimpute missing values in `xk_unobserved`, with quantified uncertainty
++ With the appropriate post-model indexing, we can use these as posterior predictions of the true values of the 
+  missing data
++ We'll show that indexing and a special comparison to the synthetic known values next
+
++++
+
+### 2.4.4 In-sample: Compare auto-imputed values $x_{k}$ `xk_unobserved` to known true values
+
++++
+
+**NOTE**
+
++ We can only compare because it's a synthetic dataset where we created those complete (full) values in `dfraw` above
+
++++
+
+#### Create index to extract appropriate obs from `xk`, because `xk_unobserved` doesn't have the right dims indexes
+
+```{code-cell} ipython3
+dfx_train_xk = (
+    dfx_train.loc[:, ["c", "d"]]
+    .reset_index()
+    .melt(id_vars=["oid"], var_name="xk_nm", value_name="xk")
+    .set_index(["oid", "xk_nm"])
+)
+idx_xk_unobs = dfx_train_xk.loc[dfx_train_xk["xk"].isnull()].index
+```
+
+##### Extract from PPC idata , isolate via index, transform back into data domain
+
+```{code-cell} ipython3
+df_mns_sdevs = pd.DataFrame({"mn": MNS, "sdev": SDEVS}, index=["a", "b", "c", "d"])
+df_mns_sdevs.index.name = "xk_nm"
+
+df_xk_unobs = (
+    az.extract(ida, group=GRP, var_names=RVS_XK)
+    .to_dataframe()
+    .drop(["chain", "draw"], axis=1)
+    .reset_index()
+    .set_index(["oid", "xk_nm"])
+    .loc[idx_xk_unobs]
+)
+df_xk_unobs = (
+    pd.merge(df_xk_unobs.reset_index(), df_mns_sdevs.reset_index(), how="left", on=["xk_nm"])
+    .set_index(["oid", "xk_nm"])
+    .sort_index()
+)
+df_xk_unobs["xk_unobs_ppc_data_domain"] = (df_xk_unobs["xk"] * df_xk_unobs["sdev"]) + df_xk_unobs[
+    "mn"
+]
+df_xk_unobs.head()
+```
+
+##### Attach real values (only available because synthetic)
+
+```{code-cell} ipython3
+dfraw_xk = (
+    dfraw[["c", "d"]]
+    .reset_index()
+    .melt(id_vars=["oid"], var_name="xk_nm", value_name="xk_unobs_true_val")
+    .set_index(["oid", "xk_nm"])
+)
+
+df_xk_unobs = pd.merge(
+    df_xk_unobs, dfraw_xk.loc[idx_xk_unobs], how="left", left_index=True, right_index=True
+)
+df_xk_unobs.head()
+```
+
+##### Force dtypes for plotting
+
+```{code-cell} ipython3
+df_xk_unobs = df_xk_unobs.reset_index()
+df_xk_unobs["oid"] = pd.Categorical(df_xk_unobs["oid"])
+df_xk_unobs["xk_nm"] = pd.Categorical(df_xk_unobs["xk_nm"])
+df_xk_unobs.set_index(["oid", "xk_nm"], inplace=True)
+df_xk_unobs.head()
+```
+
+##### Plot posterior `xk_unobserved` vs known true values (only possible in this synthetic example)
+
+```{code-cell} ipython3
+def plot_xkhat_vs_xk(
+    df_xk: pd.DataFrame,
+    xkhat: str = "xk_unobs_ppc_data_domain",
+    x: str = "xk_unobs_true_val",
+    mdlnm: str = "mdla",
+    in_samp: bool = True,
+) -> plt.Figure:
+    """Convenience plot forecast xkhat with overplotted x true val (from synthetic set)"""
+    g = sns.catplot(
+        x=xkhat,
+        y="oid",
+        col="xk_nm",
+        data=df_xk.reset_index(),
+        **KWS_BOX,
+        height=5,
+        aspect=1.5,
+        sharex=False,
+    )
+    _ = g.map(sns.scatterplot, x, "oid", **KWS_SCTR, zorder=100)
+    s = "In-sample" if in_samp else "Out-of-sample"
+    _ = g.fig.suptitle(
+        f"{s}: boxplots of posterior `{xkhat}` with overplotted actual `{x}` values"
+        + f" per observation `oid` (green dots) - `{mdlnm}`"
+    )
+    _ = g.tight_layout()
+    return g.fig
+
+
+_ = plot_xkhat_vs_xk(df_xk_unobs, mdlnm="mdla")
+```
+
+**Observe:**
+
++ Here's our auto-imputed posterior distributions (boxplots) for the missing data on the in-sample dataset `dfx_train`
++ These are a (very helpful!) side-effect of our model construction and let us fill-in the real-world missing values for
+  `c`, and `d` in `df_train`
++ Some observations (e.g. `o03`) have missing values in both `c` and `d`, others (e.g `o04`) have only one
++ We also overplot the known true values from the synthetic dataset: and the match is close for all: usually 
+  well-within the HDI94
++ Where observations have more than one missing value (e.g. `o00`, `o8`, `o10` are good examples), we see the
+  possibility of a lack of identifiability: this is an interesting and not easily avoided side-effect of the data and
+  model architecture, and in the real-world we might seek to mitigate through removing observations or features.
+
++++
+
+## 2.5 Create PPC Forecast on `dfx_holdout` set
+
++++
+
+**IMPLEMENTATION NOTE**
+
+The following process is a bit of a hack:
+
+1. Firstly: We need to re-specify the model entirely using `dfx_holdout`, because (as noted above $\S 2.1$ 
+  **Build Model Object** ) we can't put `NaNs` (or a `masked_array`) into a `pm.Data`. If we could do that then we 
+  could simply use a `pm.set_data()`, but we can't so we don't.
+2. Secondly: Sample_ppc the `xk_unobserved`, because this is a precursor to computing `yhat`, and we can't specify
+   a conditional order in `sample_posterior_predictive`
+3. Thirdly: Use those predictions to sample_ppc the `yhat`
+
+**REALITIES**
+
++ This process is suboptimal for a real-world scenario wherein we want to forecast new incoming data, because we have to
+  keep re-specifying the model in Step 1 (which opens opportunities for simple human error), and Steps 2 & 3 involve 
+  manipulations of idata objects, which is a faff
++ It should still be suitable for a relatively slow, (potentially batched) forecasting process on the order of seconds, 
+  not sub-second response times
++ In any case, if this were to be deployed to handle a stream of sub-second inputs, a much simpler way to rectify the 
+  situation would be to ensure proper data validation / hygiene upstream and require no missing data!
+
++++
+
+### 2.5.1 Firstly: Rebuild model entirely, using `dfx_holdout`
+
+```{code-cell} ipython3
+COORDS_H = deepcopy(COORDS)
+COORDS_H["oid"] = dfx_holdout.index.values
+
+with pm.Model(coords=COORDS_H) as mdla_h:
+    # 0. create (Mutable)Data containers for obs (Y, X)
+    # NOTE: You could use mdla.set_data to change these pm.Data containers...
+    y = pm.Data("y", dfx_holdout[ft_y].values, dims="oid")
+    xj = pm.Data("xj", dfx_holdout[FTS_XJ].values, dims=("oid", "xj_nm"))
+
+    # same code as above for mdla
+    # 1. create auto-imputing likelihood for missing data values
+    # NOTE: there's no way to put a nan-containing array (nor a np.masked_array)
+    # into a pm.Data, so dfx_holdout[FTS_XK].values has to go in directly
+    xk_mu = pm.Normal("xk_mu", mu=0.0, sigma=1, dims="xk_nm")
+    xk = pm.Normal(
+        "xk", mu=xk_mu, sigma=1.0, observed=dfx_holdout[FTS_XK].values, dims=("oid", "xk_nm")
+    )
+
+    # 2. define priors for contiguous and auto-imputed data
+    b_s = pm.Gamma("beta_sigma", alpha=10, beta=10)  # E ~ 1
+    bj = pm.Normal("beta_j", mu=0, sigma=b_s, dims="xj_nm")
+    bk = pm.Normal("beta_k", mu=0, sigma=b_s, dims="xk_nm")
+
+    # 4. define evidence
+    epsilon = pm.InverseGamma("epsilon", alpha=11, beta=10)
+    lm = pt.dot(xj, bj.T) + pt.dot(xk, bk.T)
+    _ = pm.Normal("yhat", mu=lm, sigma=epsilon, observed=y, dims="oid")
+```
+
+```{code-cell} ipython3
+display(pm.model_to_graphviz(mdla_h, formatting="plain"))
+assert_no_rvs(mdla_h.logp())
+mdla_h.debug(fn="logp", verbose=True)
+mdla_h.debug(fn="random", verbose=True)
+```
+
+### 2.5.2 Secondly: sample PPC for missing values `xk_unobserved` in out-of-sample dataset
+
++++
+
+**NOTE** 
+
++ Avoid changing `ida`, instead take a deepcopy `ida_h` , remove uneccessary groups, and we'll use that
++ We won't create a bare `az.InferenceData` then add groups, because we have to add all sorts of additional subtle 
+  info to the object. Easier to copy and remove groups
++ The `xarray` indexing in `posterior` will be wrong (set according to `dfx_train`, rather than `dfx_holdout`),
+  specifically dimension `oid` and coordinate `oid`.
++ Changing things like this inside an `xarray.Dataset` is a total nightmare so we won't attempt to change them. It won't
+  matter in this case anyway because we won't refer to the `posterior`.
+
+```{code-cell} ipython3
+ida_h = deepcopy(ida)
+# leave only posterior
+del (
+    ida_h.log_likelihood,
+    ida_h.sample_stats,
+    ida_h.prior,
+    ida_h.prior_predictive,
+    ida_h.posterior_predictive,
+    ida_h.observed_data,
+    ida_h.constant_data,
+)
+ida_h
+```
+
+```{code-cell} ipython3
+# NOTE for later clarity and to avoid overwriting, temporarily save posterior
+# [xk, xk_unobserved] into group `posterior_predictive`` and remove any more
+# unnecessary groups
+
+with mdla_h:
+    ida_h.extend(
+        pm.sample_posterior_predictive(
+            trace=ida_h.posterior, var_names=["xk_unobserved", "xk"], predictions=False
+        ),
+        join="right",
+    )
+
+del ida_h.observed_data, ida_h.constant_data
+ida_h
+```
+
+### 2.5.3 Thirdly: sub the predictions for `xk_unobserved` into idata and sample PPC for `yhat`
+
+```{code-cell} ipython3
+# NOTE overwrite [xk, xk_observed] in group `posterior` and sample yhat into new
+# group `predictions`
+ida_h.posterior.update({"xk_unobserved": ida_h.posterior_predictive.xk_unobserved})
+ida_h.posterior.update({"xk": ida_h.posterior_predictive.xk})
+
+with mdla_h:
+    ida_h.extend(
+        pm.sample_posterior_predictive(trace=ida_h.posterior, var_names=["yhat"], predictions=True),
+        join="right",
+    )
+
+# NOTE copy [xk, xk_observed] into group `predictions` to make it clear that these
+# dont fit in group `posterior` (the dims / coords issue), and drop groups
+# `posterior_predictive` and `posterior`
+ida_h.predictions.update({"xk_unobserved": ida_h.posterior_predictive.xk_unobserved})
+ida_h.predictions.update({"xk": ida_h.posterior_predictive.xk})
+
+del ida_h.posterior_predictive, ida_h.posterior
+ida_h
+```
+
+**Observe**
+
++ Finally in `ida_h` we have Datasets for `predictions` and `predictions_constant_data` as if we'd done only one step
++ `mdla_h` can be safely removed
+
++++
+
+### 2.5.4 Out-of-sample: Compare auto-imputed values $x_{k}$ `xk_unobserved` to known true values
+
++++
+
+**NOTE**
+
++ Recall we can only do this because it's a synthetic dataset where we created those complete (full) values in `dfraw` above
++ We will show a plot that might surprise the reader...
+
++++
+
+##### Create index to extract appropriate obs from `xk`, because `xk_unobserved` doesn't have the right dims indexes
+
+```{code-cell} ipython3
+dfx_holdout_xk = (
+    dfx_holdout.loc[:, ["c", "d"]]
+    .reset_index()
+    .melt(id_vars=["oid"], var_name="xk_nm", value_name="xk")
+    .set_index(["oid", "xk_nm"])
+)
+idx_h_xk_unobs = dfx_holdout_xk.loc[dfx_holdout_xk["xk"].isnull()].index
+```
+
+##### Extract `xk_unobserved` from PPC idata , isolate via index, transform back into data domain
+
+```{code-cell} ipython3
+df_h_xk_unobs = (
+    az.extract(ida_h, group="predictions", var_names=["xk"])
+    .to_dataframe()
+    .drop(["chain", "draw"], axis=1)
+    .reset_index()
+    .set_index(["oid", "xk_nm"])
+    .loc[idx_h_xk_unobs]
+)
+df_h_xk_unobs = (
+    pd.merge(df_h_xk_unobs.reset_index(), df_mns_sdevs.reset_index(), how="left", on=["xk_nm"])
+    .set_index(["oid", "xk_nm"])
+    .sort_index()
+)
+df_h_xk_unobs["xk_unobs_ppc_data_domain"] = (
+    df_h_xk_unobs["xk"] * df_h_xk_unobs["sdev"]
+) + df_h_xk_unobs["mn"]
+```
+
+##### Attach real values (only possible in this synthetic example)
+
+```{code-cell} ipython3
+df_h_xk_unobs = pd.merge(
+    df_h_xk_unobs, dfraw_xk.loc[idx_h_xk_unobs], how="left", left_index=True, right_index=True
+)
+```
+
+##### Force dtypes for plotting
+
+```{code-cell} ipython3
+df_h_xk_unobs = df_h_xk_unobs.reset_index()
+df_h_xk_unobs["oid"] = pd.Categorical(df_h_xk_unobs["oid"])
+df_h_xk_unobs["xk_nm"] = pd.Categorical(df_h_xk_unobs["xk_nm"])
+df_h_xk_unobs.set_index(["oid", "xk_nm"], inplace=True)
+```
+
+##### Plot posterior `xk_unobserved` vs known true values (only possible in this synthetic example)
+
+```{code-cell} ipython3
+_ = plot_xkhat_vs_xk(df_h_xk_unobs, mdlnm="mdla", in_samp=False)
+```
+
+**Observe:**
+
++ Excellent: this looks like we have a terrible prediction - but the model is working as expected, and this is helpful
+  to illustrate
++ The posterior values of `xk_unobserved` have been pulled from the hierarchical `xk_mu` distributions and are not 
+  conditional on anything else, so we get pretty much all the same predicted value
++ This should drive home the understanding that while technically this model **can** handle new missing values,
+  and **does** auto-impute values for missing data in an out-of-sample dataset (here `dfx_holdout`), these auto-imputed
+  values for `xk_unobserved` **can't** be any more informative than the posterior distribution of the hierachical 
+  prior `xk_mu`.
+
++++
+
+### 2.5.5 Out-of-sample: Compare forecasted $\hat{y}$ `yhat` to known true values
+
++++
+
+##### Extract `yhat` from PPC idata, and attach real values (only available because it's a `holdout` set)
+
+```{code-cell} ipython3
+df_h_y = (
+    az.extract(ida_h, group="predictions", var_names=["yhat"])
+    .to_dataframe()
+    .drop(["chain", "draw"], axis=1)
+    .reset_index()
+    .set_index(["oid"])
+)
+df_h_y = pd.merge(df_h_y, df_holdout[["y"]], how="left", left_index=True, right_index=True)
+df_h_y.describe().T
+```
+
+##### Plot posterior `yhat` vs known true values `y` (only available because it's a `holdout` set)
+
+```{code-cell} ipython3
+_ = plot_yhat_vs_y(df_h_y)
+```
+
+**Observe:**
+
++ The predictions `yhat` look pretty close to the true value `y`, usually well-within the $HDI_{94}$
++ As we would expect, the distributions of `yhat` are useful too: quantifing the uncertainty in prediction and letting us
+  make better decisions accordingly.
+
++++
+
+---
+
+---
+
++++
+
+# Errata
+
+## Authors
+
++ Created by [Jonathan Sedar](https://github.com/jonsedar) in Nov 2024
+
+## Reference
+
+:::{bibliography}
+:filter: docname in docnames
+:::
+
++++
+
+## Watermark
+
+```{code-cell} ipython3
+# tested running on Google Colab 2024-12-16
+%load_ext watermark
+%watermark -n -u -v -iv -w
+```
+
+:::{include} ../page_footer.md
+:::
diff --git a/examples/references.bib b/examples/references.bib
index 5c7726b7..b9218fc6 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -11,7 +11,7 @@ @article{ando2007bayesian
 }
 @book{angrist2008harmless,
   title         = {Mostly Harmless Econometrics},
-  author        = {Angrist, Joshua and Pischke, Jörn-Steffen},
+  author        = {Angrist, Joshua and Pischke, J\"{o}rn-Steffen},
   year          = {2008},
   publisher     = {Princeton University Press}
 }
@@ -94,6 +94,14 @@ @book{breen1996regression
   year          = {1996},
   publisher     = {Sage}
 }
+@article{burkner2018,
+  title         = {Modeling Monotonic Effects of Ordinal Predictors in Bayesian Regression Models},
+  author        = {B{\"u}rkner P, \& Charpentier E},
+  year          = {2018},
+  journal       = {PsyArXiv},
+  url           = {https://doi.org/10.31234/osf.io/9qkhj},
+  doi           = {doi:10.31234/osf.io/9qkhj}
+}
 @inproceedings{caprani2009gev,
   title         = "Estimating extreme highway bridge traffic load effects",
   author        = {Colin C. Caprani and Eugene J. OBrien},
@@ -322,9 +330,9 @@ @book{hayes2017introduction
 }
 @book{hernan2020whatif,
   title         = {Causal Inference: What If},
-  author        = {Hernán, MA and Robins, JM},
+  author        = {Hern\'{a}n, MA and Robins, JM},
   year          = {2020},
-  publisher     = {Chapman & Hall/CRC}
+  publisher     = {Chapman \& Hall/CRC}
 }
 @article{hoffman2014nuts,
   title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
@@ -407,6 +415,13 @@ @article{johnson1999
   title         = {The Insignificance of Statistical Significance Testing},
   journal       = {The Journal of Wildlife Management}
 }
+@online{junpenglao2020,
+  title         = {Partial Missing Multivariate Observation and What to Do With Them},
+  author        = {Lao, Junpeng},
+  url           = {https://discourse.pymc.io/t/partial-missing-multivariate-observation-and-what-to-do-with-them-by-junpeng-lao/6050},
+  urldate       = {2020-10-01},
+  year          = {2020}
+}
 @manual{kassambara2019datarium,
   title         = {datarium: Data Bank for Statistical Analysis and Visualization},
   author        = {Alboukadel Kassambara},
@@ -618,6 +633,17 @@ @article{nuijten2015default
   year          = {2015},
   publisher     = {Springer}
 }
+@online{numpyroBirthdays,
+  title         = {Example: Hilbert space approximation for Gaussian processes},
+  url           = {https://num.pyro.ai/en/stable/examples/hsgp.html}
+}
+@online{orduz2024Birthdays,
+  title         = {Time Series Modeling with HSGP: Baby Births Example},
+  author        = {Orduz, Juan},
+  url           = {https://juanitorduz.github.io/birthdays/},
+  urldate       = {2024-01-02},
+  year          = {2024}
+}
 @unpublished{padonou2015polar,
   title         = {{Polar Gaussian Processes for Predicting on Circular Domains}},
   author        = {Padonou, Esp{\'e}ran and Roustant, O},
@@ -652,6 +678,21 @@ @book{pearl2018why
   year          = {2018},
   publisher     = {Basic books}
 }
+@article{penrose1985,
+  title         = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}},
+  volume        = {17},
+  issn          = {0195-9131},
+  url           = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx},
+  abstract      = {An abstract is unavailable. This article is available as a PDF only.},
+  language      = {en-US},
+  number        = {2},
+  urldate       = {2023-10-17},
+  journal       = {Medicine \& Science in Sports \& Exercise},
+  author        = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.},
+  month         = apr,
+  year          = {1985},
+  pages         = {189}
+}
 @misc{quiroga2022bart,
   title         = {Bayesian additive regression trees for probabilistic programming},
   author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
@@ -680,6 +721,17 @@ @article{riebler2016intuitive
   year          = {2016},
   publisher     = {Sage Publications Sage UK: London, England}
 }
+@article{riutort2022PracticalHilbertSpaceApproximate,
+  author        = {Riutort-Mayol, Gabriel and B{\"u}rkner, Paul-Christian and Andersen, Michael R. and Solin, Arno and Vehtari, Aki},
+  doi           = {10.1007/s11222-022-10167-2},
+  journal       = {Statistics and Computing},
+  number        = {1},
+  pages         = {17},
+  title         = {Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming},
+  url           = {https://doi.org/10.1007/s11222-022-10167-2},
+  volume        = {33},
+  year          = {2022}
+}
 @book{roback2021beyond,
   title         = {Beyond multiple linear regression: Applied generalized linear models and multilevel models in R},
   author        = {Roback, P., and Legler, J.},
@@ -687,6 +739,10 @@ @book{roback2021beyond
   publisher     = {CRC Press},
   url           = {https://bookdown.org/roback/bookdown-BeyondMLR/}
 }
+@online{rochford2018,
+  author        = {Austin Rochford},
+  url           = {https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html}
+}
 @inproceedings{salakhutdinov2008bayesian,
   title         = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo},
   author        = {Salakhutdinov, Ruslan and Mnih, Andriy},
@@ -710,6 +766,17 @@ @book{singer2003
   year          = {2003},
   publisher     = {Oxford University Press}
 }
+@article{solin2020Hilbert,
+  author        = {Solin, Arno and S{\"a}rkk{\"a}, Simo},
+  doi           = {10.1007/s11222-019-09886-w},
+  journal       = {Statistics and Computing},
+  number        = {2},
+  pages         = {419--446},
+  title         = {Hilbert space methods for reduced-rank Gaussian process regression},
+  url           = {https://doi.org/10.1007/s11222-019-09886-w},
+  volume        = {30},
+  year          = {2020}
+}
 @article{spiller2013spotlights,
   title         = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression},
   author        = {Spiller, Stephen A and Fitzsimons, Gavan J and Lynch Jr, John G and McClelland, Gary H},
@@ -793,6 +860,13 @@ @inproceedings{vanderplas2012astroML
   doi           = {10.1109/CIDU.2012.6382200},
   year          = {2012}
 }
+@online{vehtari2022Birthdays,
+  title         = {Bayesian workflow book - Birthdays},
+  author        = {Vehtari, Aki},
+  url           = {https://avehtari.github.io/casestudies/Birthdays/birthdays.html},
+  urldate       = {2022-03-07},
+  year          = {2022}
+}
 @online{vieira2022BVAR,
   title         = {Bayesian Vector Autoregression in PyMC},
   author        = {Vieira, Ricardo},
@@ -841,6 +915,19 @@ @article{Yao_2018
   title         = {Using Stacking to Average Bayesian Predictive Distributions (with Discussion)},
   journal       = {Bayesian Analysis}
 }
+@article{Yao_2022,
+  author        = {Yuling Yao and Gregor Pir\v{s} and Aki Vehtari and Andrew Gelman},
+  title         = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}},
+  volume        = {17},
+  journal       = {Bayesian Analysis},
+  number        = {4},
+  publisher     = {International Society for Bayesian Analysis},
+  pages         = {1043 -- 1071},
+  keywords      = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking},
+  year          = {2022},
+  doi           = {10.1214/21-BA1287},
+  url           = {https://doi.org/10.1214/21-BA1287}
+}
 @article{yuan2009bayesian,
   title         = {Bayesian mediation analysis.},
   author        = {Yuan, Ying and MacKinnon, David P},
@@ -867,87 +954,3 @@ @article{zhang2021pathfinder
   journal       = {arXiv preprint arXiv:2108.03782},
   year          = {2021}
 }
-@online{orduz2024Birthdays,
-  title         = {Time Series Modeling with HSGP: Baby Births Example},
-  author        = {Orduz, Juan},
-  url           = {https://juanitorduz.github.io/birthdays/},
-  urldate       = {2024-01-02},
-  year          = {2024}
-}
-@online{vehtari2022Birthdays,
-  title         = {Bayesian workflow book - Birthdays},
-  author        = {Vehtari, Aki},
-  url           = {https://avehtari.github.io/casestudies/Birthdays/birthdays.html},
-  urldate       = {2022-03-07},
-  year          = {2022}
-}
-@online{numpyroBirthdays,
-  title         = {Example: Hilbert space approximation for Gaussian processes},
-  url           = {https://num.pyro.ai/en/stable/examples/hsgp.html},
-}
-@article{riutort2022PracticalHilbertSpaceApproximate,
-	author = {Riutort-Mayol, Gabriel and B{\"u}rkner, Paul-Christian and Andersen, Michael R. and Solin, Arno and Vehtari, Aki},
-	doi = {10.1007/s11222-022-10167-2},
-	journal = {Statistics and Computing},
-	number = {1},
-	pages = {17},
-	title = {Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming},
-	url = {https://doi.org/10.1007/s11222-022-10167-2},
-	volume = {33},
-	year = {2022},
-}
-@article{solin2020Hilbert,
-	author = {Solin, Arno and S{\"a}rkk{\"a}, Simo},
-	doi = {10.1007/s11222-019-09886-w},
-	journal = {Statistics and Computing},
-	number = {2},
-	pages = {419--446},
-	title = {Hilbert space methods for reduced-rank Gaussian process regression},
-	url = {https://doi.org/10.1007/s11222-019-09886-w},
-	volume = {30},
-	year = {2020},
-}
-
-@article{penrose1985,
-	title = {{GENERALIZED} {BODY} {COMPOSITION} {PREDICTION} {EQUATION} {FOR} {MEN} {USING} {SIMPLE} {MEASUREMENT} {TECHNIQUES}},
-	volume = {17},
-	issn = {0195-9131},
-	url = {https://journals.lww.com/acsm-msse/citation/1985/04000/generalized_body_composition_prediction_equation.37.aspx},
-	abstract = {An abstract is unavailable. This article is available as a PDF only.},
-	language = {en-US},
-	number = {2},
-	urldate = {2023-10-17},
-	journal = {Medicine \& Science in Sports \& Exercise},
-	author = {Penrose, K. W. and Nelson, A. G. and Fisher, A. G.},
-	month = apr,
-	year = {1985},
-	pages = {189},
-}
-
-@article{Yao_2022,
-author = {Yuling Yao and Gregor Pirš and Aki Vehtari and Andrew Gelman},
-title = {{Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful}},
-volume = {17},
-journal = {Bayesian Analysis},
-number = {4},
-publisher = {International Society for Bayesian Analysis},
-pages = {1043 -- 1071},
-keywords = {Bayesian hierarchical modeling, conditional prediction, covariate shift, model averaging, prior construction, stacking},
-year = {2022},
-doi = {10.1214/21-BA1287},
-URL = {https://doi.org/10.1214/21-BA1287}
-}
-
-@article{burkner2018,
-title = {Modeling Monotonic Effects of Ordinal Predictors in Bayesian Regression Models}, 
-author = {B{\"u}rkner P, & Charpentier E},
-year = {2018},
-journal = {PsyArXiv},
-url = {https://doi.org/10.31234/osf.io/9qkhj},
-doi = {doi:10.31234/osf.io/9qkhj},
-}
-
-@online{rochford2018,
-author={Austin Rochford},
-url={https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html},
-}
\ No newline at end of file