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Example in which epsilon actually does something #7

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richelbilderbeek opened this issue Jan 29, 2020 · 2 comments
Open

Example in which epsilon actually does something #7

richelbilderbeek opened this issue Jan 29, 2020 · 2 comments

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@richelbilderbeek
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richelbilderbeek commented Jan 29, 2020 •
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Hi Remco,

I would enjoy an example that uses NS in which a change of epsilon would have an effect, because whatever I do (example here), I cannot find a setting in which changing the epsilon has an effect.

As I example, I supply these files as a zip, and explain here.

There are two BEAST2 .xml files: ns_1.xml and ns_2.xml. Using diff from a terminal like this (richel@sonic:~$ is my prompt)...

richel@sonic:~$ diff ns_1.xml ns_2.xml

I see that these files only dffer in their epsilon ...

41c41
< <run id="mcmc" spec="beast.gss.NS" chainLength="10000000000" storeEvery="1000" particleCount="1" subChainLength="5000" epsilon="100">
---
> <run id="mcmc" spec="beast.gss.NS" chainLength="10000000000" storeEvery="1000" particleCount="1" subChainLength="5000" epsilon="1">

and further on only the files they create:

95c95
<     <logger id="tracelog" fileName="/home/richel/ns_1.log" logEvery="1000" model="@posterior" sanitiseHeaders="true" sort="smart">
---
>     <logger id="tracelog" fileName="/home/richel/ns_2.log" logEvery="1000" model="@posterior" sanitiseHeaders="true" sort="smart">
105c105
<     <logger id="screenlog" fileName="/home/richel/ns_1.txt" logEvery="1000">
---
>     <logger id="screenlog" fileName="/home/richel/ns_2.txt" logEvery="1000">
112c112
<     <logger id="treelog.t:primates" fileName="/home/richel/ns_1.trees" logEvery="1000" mode="tree">
---
>     <logger id="treelog.t:primates" fileName="/home/richel/ns_2.trees" logEvery="1000" mode="tree">

Running both using BEAST2 (v2.6.1 with NS version 1.1.0) from the command line like this ...

beast -seed 314 ns_2.xml

... runs just fine (output pasted below). When I check the files for differences, however, there are no differences:

richel@sonic:~$ diff ns_1.log ns_2.log 
richel@sonic:~$ diff ns_1.posterior.trees ns_2.posterior.trees 
richel@sonic:~$ diff ns_1.posterior.txt ns_2.posterior.txt
richel@sonic:~$ diff ns_1.trees ns_2.trees 
richel@sonic:~$ diff ns_1.txt ns_2.txt

Could you show me an example in which epsilon actually does something? Would be super helpful to me!

Thanks, Richel

Output

(./beast is a symbolic link to BEAST2)

richel@sonic:~$ ./beast -seed 314 ns_1.xml 

                        BEAST v2.6.1, 2002-2019
             Bayesian Evolutionary Analysis Sampling Trees
                       Designed and developed by
 Remco Bouckaert, Alexei J. Drummond, Andrew Rambaut & Marc A. Suchard
                                    
                   Centre for Computational Evolution
                         University of Auckland
                       [email protected]
                        [email protected]
                                    
                   Institute of Evolutionary Biology
                        University of Edinburgh
                           [email protected]
                                    
                    David Geffen School of Medicine
                 University of California, Los Angeles
                           [email protected]
                                    
                      Downloads, Help & Resources:
                           http://beast2.org/
                                    
  Source code distributed under the GNU Lesser General Public License:
                   http://github.com/CompEvol/beast2
                                    
                           BEAST developers:
   Alex Alekseyenko, Trevor Bedford, Erik Bloomquist, Joseph Heled, 
 Sebastian Hoehna, Denise Kuehnert, Philippe Lemey, Wai Lok Sibon Li, 
Gerton Lunter, Sidney Markowitz, Vladimir Minin, Michael Defoin Platel, 
          Oliver Pybus, Tim Vaughan, Chieh-Hsi Wu, Walter Xie
                                    
                               Thanks to:
          Roald Forsberg, Beth Shapiro and Korbinian Strimmer

Random number seed: 314

File: ns_1.xml seed: 314 threads: 1
Loading package MM v1.1.1
Loading package SA v2.0.2
Loading package BEAST v2.6.1
Loading package NS v1.1.0
Loading package BEASTLabs v1.9.1
Loading package TreeStat2 v0.0.2
Loading package MODEL_SELECTION v1.5.2
Loading package starbeast2 v0.15.5
Loading package BEAST v2.6.1
Gorilla: 898 4
Homo_sapiens: 898 4
Hylobates: 898 4
Lemur_catta: 898 4
M_fascicularis: 898 4
M_mulatta: 898 4
M_sylvanus: 898 4
Macaca_fuscata: 898 4
Pan: 898 4
Pongo: 898 4
Saimiri_sciureus: 898 4
Tarsius_syrichta: 898 4
Alignment(primates)
  12 taxa
  898 sites
  413 patterns

  Using BEAGLE version: 3.1.2 resource 0: CPU
    with instance flags:  PRECISION_DOUBLE COMPUTATION_SYNCH EIGEN_REAL SCALING_MANUAL SCALERS_RAW VECTOR_SSE THREADING_NONE PROCESSOR_CPU FRAMEWORK_CPU
  Ignoring ambiguities in tree likelihood.
  Ignoring character uncertainty in tree likelihood.
  With 413 unique site patterns.
  Using rescaling scheme : dynamic
===============================================================================
Citations for this model:

Patricio Maturana, Brendon J. Brewer, Steffen Klaere, Remco Bouckaert. Model selection and parameter inference in phylogenetics using Nested Sampling. Systematic Biology, syy050, 2018

===============================================================================
Counting 47 parameters
WARNING: If nothing seems to be happening on screen this is because none of the loggers give feedback to screen.
WARNING: This happens when a filename  is specified for the 'screenlog' logger.
WARNING: To get feedback to screen, leave the filename for screenlog blank.
WARNING: Otherwise, the screenlog is saved into the specified file.
replacing logger screenlog with NSLogger
replacing logger tracelog with NSLogger
Writing file /home/richel/ns_1.trees
Writing file /home/richel/ns_1.txt
Writing file /home/richel/ns_1.log
Start likelihood 0: -9235.717347561726 
1 particles initialised
ML: -12584.711551152224 Information: 0.838560638429044
ML: -8637.946077067045 Information: 1.838560638429044
ML: -8553.024538953608 Information: 2.838560638429044
ML: -8518.338515092692 Information: 3.8385606384216646
ML: -8283.597397842 Information: 4.838560638429044
ML: -8217.293509037343 Information: 5.838560638429044
ML: -7692.8777700016735 Information: 6.838560638429044
ML: -7610.170022526167 Information: 7.838560638429044
ML: -7507.255061095257 Information: 8.838560638429044
ML: -7440.754661924838 Information: 9.838560638429044
ML: -7310.076598179651 Information: 10.838560638429044
ML: -7246.250217541103 Information: 11.838560638429044
ML: -7164.353538623825 Information: 12.838560638429044
ML: -7044.053487843343 Information: 13.838560638429044
ML: -6916.555976946184 Information: 14.838560638429044
ML: -6916.154398692765 Information: 14.534598312397065
ML: -6867.66249013794 Information: 16.838560638429044
ML: -6867.1182867293965 Information: 16.578063041788027
ML: -6866.379148844656 Information: 17.066978947466396
ML: -6834.037410851616 Information: 19.83856063836741
ML: -6813.8748362185825 Information: 20.838560598270366
ML: -6757.817684813298 Information: 21.838560638429044
ML: -6734.555476082779 Information: 22.838560637616855
ML: -6617.595860517789 Information: 23.838560638429044
ML: -6607.845726953702 Information: 24.837875760962756
ML: -6607.192220426984 Information: 24.625656547920244
ML: -6571.196554576036 Information: 26.83856063842751
ML: -6570.57271513831 Information: 26.61210785852063
ML: -6547.344669057432 Information: 28.838560637317443
ML: -6539.271761657847 Information: 29.83541919740043
ML: -6536.659127788635 Information: 30.502792249567165
ML: -6532.861465684524 Information: 31.701283208443073
ML: -6517.677544326023 Information: 32.83855623024205
ML: -6509.69979708208 Information: 33.83513821620812
ML: -6503.768191168093 Information: 34.81750288614057
ML: -6502.456714774669 Information: 34.98078213827603
ML: -6490.897997266643 Information: 36.83842292901169
ML: -6490.5130731414465 Information: 36.531474377279665
ML: -6490.196915224223 Information: 36.5725171855529
ML: -6490.062693758215 Information: 36.604800960477405
ML: -6490.003451880488 Information: 36.62824010598524
ML: -6488.925619763848 Information: 39.424063286751334
ML: -6486.882082702129 Information: 42.01057784948614
ML: -6484.219680984177 Information: 43.45793260585634
ML: -6483.853515573744 Information: 43.264886698074406
ML: -6483.430279255121 Information: 43.50865502425131
ML: -6483.102305328808 Information: 43.84716487022979
ML: -6482.912902258525 Information: 44.075966527285345
ML: -6482.8495340805375 Information: 44.13759178609871
ML: -6482.826771827252 Information: 44.15825816312463
ML: -6482.810451128966 Information: 44.18359226979646
ML: -6482.8032359453355 Information: 44.19598215492897
ML: -6482.792893427116 Information: 44.227581187399664
ML: -6482.786639311099 Information: 44.249629615353115
ML: -6482.778862680543 Information: 44.28628820938411
ML: -6482.7759956244145 Information: 44.2997377834472
ML: -6482.774908599106 Information: 44.30485979387049
ML: -6482.774458704875 Information: 44.30703103883752
ML: -6482.77426513005 Information: 44.30799527360887
ML: -6482.774190918482 Information: 44.3083679479214
ML: -6482.77416052567 Information: 44.30852382861758
ML: -6482.77414842891 Information: 44.308586820079654
ML: -6482.77414335373 Information: 44.308613914925445
ML: -6482.774141415386 Information: 44.308624334535125
ML: -6482.77414065967 Information: 44.30862844009698
ML: -6482.774140361039 Information: 44.30863008275901
ML: -6482.774140249349 Information: 44.30863069983752
ML: -6482.774140206997 Information: 44.30863093786775
ML: -6482.774140191096 Information: 44.30863102409876
ML: -6482.774140184793 Information: 44.30863106187189
ML: -6482.774140182447 Information: 44.308631072378375
ML: -6482.774140181578 Information: 44.308631075062294
ML: -6482.774140181258 Information: 44.30863107868845
ML: -6482.77414018114 Information: 44.30863108119138
ML: -6482.774140181096 Information: 44.30863108246649
ML: -6482.77414018108 Information: 44.3086310848239
ML: -6482.774140181074 Information: 44.30863108771882
ML: -6482.774140181072 Information: 44.308631085395064
ML: -6482.774140181071 Information: 44.308631086077185
ML: -6482.774140181071 Information: 44.30863108415906
ML: -6482.774140181071 Information: 44.30863108345329
ML: -6482.774140181071 Information: 44.30863108319318
ML: -6482.774140181071 Information: 44.30863108309768
ML: -6482.774140181071 Information: 44.30863108306221
ML: -6482.774140181071 Information: 44.30863108304948
ML: -6482.774140181071 Information: 44.30863108304493
ML: -6482.774140181071 Information: 44.30863108304311
ML: -6482.774140181071 Information: 44.3086310830422
ML: -6482.774140181071 Information: 44.3086310830422
89<=10000000000&& (89<2.0*44.3086310830422*1||Math.abs(-6482.774140181071 - -6482.774140181071)/Math.abs(-6482.774140181071) > 100.0
Finished in 89 steps!
Marginal likelihood: -6482.258778300394 (bootstrap SD=6.3740821217718935)
Marginal likelihood: -6481.429556962358 (subsample SD=7.717558298097089)
Marginal likelihood: -6482.785768124018(6.833409275448446)
Information: 44.3086310830422
SD: 6.65647287105132

Operator                                              Tuning    #accept    #reject      Pr(m)  Pr(acc|m)
ScaleOperator(YuleBirthRateScaler.t:primates)        0.75000      13657       4192    0.04000    0.76514 Try setting scaleFactor to about 0.562
ScaleOperator(YuleModelTreeScaler.t:primates)        0.50000       2467      15756    0.04000    0.13538 
ScaleOperator(YuleModelTreeRootScaler.t:primates)    0.50000       1610      16457    0.04000    0.08911 Try setting scaleFactor to about 0.707
Uniform(YuleModelUniformOperator.t:primates)               -      59013     121435    0.40000    0.32704 
SubtreeSlide(YuleModelSubtreeSlide.t:primates)       1.00000       1318      88184    0.20000    0.01473 Try decreasing size to about 0.5
Exchange(YuleModelNarrow.t:primates)                       -      21871      68001    0.20000    0.24336 
Exchange(YuleModelWide.t:primates)                         -       1004      17075    0.04000    0.05553 
WilsonBalding(YuleModelWilsonBalding.t:primates)           -        885      17075    0.04000    0.04928 

     Tuning: The value of the operator's tuning parameter, or '-' if the operator can't be optimized.
    #accept: The total number of times a proposal by this operator has been accepted.
    #reject: The total number of times a proposal by this operator has been rejected.
      Pr(m): The probability this operator is chosen in a step of the MCMC (i.e. the normalized weight).
  Pr(acc|m): The acceptance probability (#accept as a fraction of the total proposals for this operator).


Total calculation time: 16.081 seconds
End likelihood: 11.219717578767929
Producing posterior samples

Marginal likelihood: -6481.810657013448 sqrt(H/N)=(6.489167542270841)=?=SD=(6.671709390277811) Information: 42.109295391661384
Max ESS: 7.905853498440624


Processing 89 trees from file.
Log file written to /home/richel/ns_1.posterior.trees
Done!

Marginal likelihood: -6482.047859072695 sqrt(H/N)=(6.504666448161965)=?=SD=(6.820779663704277) Information: 42.310685601843986
Max ESS: 7.744327344657105


Log file written to /home/richel/ns_1.posterior.txt
Done!

Marginal likelihood: -6482.01338247287 sqrt(H/N)=(6.488946466034643)=?=SD=(6.3878109899164235) Information: 42.10642623906348
Max ESS: 7.742249401124373


Log file written to /home/richel/ns_1.posterior.txt
Done!
richel@sonic:~$ ./beast -seed 314 ns_2.xml 

                        BEAST v2.6.1, 2002-2019
             Bayesian Evolutionary Analysis Sampling Trees
                       Designed and developed by
 Remco Bouckaert, Alexei J. Drummond, Andrew Rambaut & Marc A. Suchard
                                    
                   Centre for Computational Evolution
                         University of Auckland
                       [email protected]
                        [email protected]
                                    
                   Institute of Evolutionary Biology
                        University of Edinburgh
                           [email protected]
                                    
                    David Geffen School of Medicine
                 University of California, Los Angeles
                           [email protected]
                                    
                      Downloads, Help & Resources:
                           http://beast2.org/
                                    
  Source code distributed under the GNU Lesser General Public License:
                   http://github.com/CompEvol/beast2
                                    
                           BEAST developers:
   Alex Alekseyenko, Trevor Bedford, Erik Bloomquist, Joseph Heled, 
 Sebastian Hoehna, Denise Kuehnert, Philippe Lemey, Wai Lok Sibon Li, 
Gerton Lunter, Sidney Markowitz, Vladimir Minin, Michael Defoin Platel, 
          Oliver Pybus, Tim Vaughan, Chieh-Hsi Wu, Walter Xie
                                    
                               Thanks to:
          Roald Forsberg, Beth Shapiro and Korbinian Strimmer

Random number seed: 314

File: ns_2.xml seed: 314 threads: 1
Loading package MM v1.1.1
Loading package SA v2.0.2
Loading package BEAST v2.6.1
Loading package NS v1.1.0
Loading package BEASTLabs v1.9.1
Loading package TreeStat2 v0.0.2
Loading package MODEL_SELECTION v1.5.2
Loading package starbeast2 v0.15.5
Loading package BEAST v2.6.1
Gorilla: 898 4
Homo_sapiens: 898 4
Hylobates: 898 4
Lemur_catta: 898 4
M_fascicularis: 898 4
M_mulatta: 898 4
M_sylvanus: 898 4
Macaca_fuscata: 898 4
Pan: 898 4
Pongo: 898 4
Saimiri_sciureus: 898 4
Tarsius_syrichta: 898 4
Alignment(primates)
  12 taxa
  898 sites
  413 patterns

  Using BEAGLE version: 3.1.2 resource 0: CPU
    with instance flags:  PRECISION_DOUBLE COMPUTATION_SYNCH EIGEN_REAL SCALING_MANUAL SCALERS_RAW VECTOR_SSE THREADING_NONE PROCESSOR_CPU FRAMEWORK_CPU
  Ignoring ambiguities in tree likelihood.
  Ignoring character uncertainty in tree likelihood.
  With 413 unique site patterns.
  Using rescaling scheme : dynamic
===============================================================================
Citations for this model:

Patricio Maturana, Brendon J. Brewer, Steffen Klaere, Remco Bouckaert. Model selection and parameter inference in phylogenetics using Nested Sampling. Systematic Biology, syy050, 2018

===============================================================================
Counting 47 parameters
WARNING: If nothing seems to be happening on screen this is because none of the loggers give feedback to screen.
WARNING: This happens when a filename  is specified for the 'screenlog' logger.
WARNING: To get feedback to screen, leave the filename for screenlog blank.
WARNING: Otherwise, the screenlog is saved into the specified file.
replacing logger screenlog with NSLogger
replacing logger tracelog with NSLogger
Writing file /home/richel/ns_2.trees
Writing file /home/richel/ns_2.txt
Writing file /home/richel/ns_2.log
Start likelihood 0: -9235.717347561726 
1 particles initialised
ML: -12584.711551152224 Information: 0.838560638429044
ML: -8637.946077067045 Information: 1.838560638429044
ML: -8553.024538953608 Information: 2.838560638429044
ML: -8518.338515092692 Information: 3.8385606384216646
ML: -8283.597397842 Information: 4.838560638429044
ML: -8217.293509037343 Information: 5.838560638429044
ML: -7692.8777700016735 Information: 6.838560638429044
ML: -7610.170022526167 Information: 7.838560638429044
ML: -7507.255061095257 Information: 8.838560638429044
ML: -7440.754661924838 Information: 9.838560638429044
ML: -7310.076598179651 Information: 10.838560638429044
ML: -7246.250217541103 Information: 11.838560638429044
ML: -7164.353538623825 Information: 12.838560638429044
ML: -7044.053487843343 Information: 13.838560638429044
ML: -6916.555976946184 Information: 14.838560638429044
ML: -6916.154398692765 Information: 14.534598312397065
ML: -6867.66249013794 Information: 16.838560638429044
ML: -6867.1182867293965 Information: 16.578063041788027
ML: -6866.379148844656 Information: 17.066978947466396
ML: -6834.037410851616 Information: 19.83856063836741
ML: -6813.8748362185825 Information: 20.838560598270366
ML: -6757.817684813298 Information: 21.838560638429044
ML: -6734.555476082779 Information: 22.838560637616855
ML: -6617.595860517789 Information: 23.838560638429044
ML: -6607.845726953702 Information: 24.837875760962756
ML: -6607.192220426984 Information: 24.625656547920244
ML: -6571.196554576036 Information: 26.83856063842751
ML: -6570.57271513831 Information: 26.61210785852063
ML: -6547.344669057432 Information: 28.838560637317443
ML: -6539.271761657847 Information: 29.83541919740043
ML: -6536.659127788635 Information: 30.502792249567165
ML: -6532.861465684524 Information: 31.701283208443073
ML: -6517.677544326023 Information: 32.83855623024205
ML: -6509.69979708208 Information: 33.83513821620812
ML: -6503.768191168093 Information: 34.81750288614057
ML: -6502.456714774669 Information: 34.98078213827603
ML: -6490.897997266643 Information: 36.83842292901169
ML: -6490.5130731414465 Information: 36.531474377279665
ML: -6490.196915224223 Information: 36.5725171855529
ML: -6490.062693758215 Information: 36.604800960477405
ML: -6490.003451880488 Information: 36.62824010598524
ML: -6488.925619763848 Information: 39.424063286751334
ML: -6486.882082702129 Information: 42.01057784948614
ML: -6484.219680984177 Information: 43.45793260585634
ML: -6483.853515573744 Information: 43.264886698074406
ML: -6483.430279255121 Information: 43.50865502425131
ML: -6483.102305328808 Information: 43.84716487022979
ML: -6482.912902258525 Information: 44.075966527285345
ML: -6482.8495340805375 Information: 44.13759178609871
ML: -6482.826771827252 Information: 44.15825816312463
ML: -6482.810451128966 Information: 44.18359226979646
ML: -6482.8032359453355 Information: 44.19598215492897
ML: -6482.792893427116 Information: 44.227581187399664
ML: -6482.786639311099 Information: 44.249629615353115
ML: -6482.778862680543 Information: 44.28628820938411
ML: -6482.7759956244145 Information: 44.2997377834472
ML: -6482.774908599106 Information: 44.30485979387049
ML: -6482.774458704875 Information: 44.30703103883752
ML: -6482.77426513005 Information: 44.30799527360887
ML: -6482.774190918482 Information: 44.3083679479214
ML: -6482.77416052567 Information: 44.30852382861758
ML: -6482.77414842891 Information: 44.308586820079654
ML: -6482.77414335373 Information: 44.308613914925445
ML: -6482.774141415386 Information: 44.308624334535125
ML: -6482.77414065967 Information: 44.30862844009698
ML: -6482.774140361039 Information: 44.30863008275901
ML: -6482.774140249349 Information: 44.30863069983752
ML: -6482.774140206997 Information: 44.30863093786775
ML: -6482.774140191096 Information: 44.30863102409876
ML: -6482.774140184793 Information: 44.30863106187189
ML: -6482.774140182447 Information: 44.308631072378375
ML: -6482.774140181578 Information: 44.308631075062294
ML: -6482.774140181258 Information: 44.30863107868845
ML: -6482.77414018114 Information: 44.30863108119138
ML: -6482.774140181096 Information: 44.30863108246649
ML: -6482.77414018108 Information: 44.3086310848239
ML: -6482.774140181074 Information: 44.30863108771882
ML: -6482.774140181072 Information: 44.308631085395064
ML: -6482.774140181071 Information: 44.308631086077185
ML: -6482.774140181071 Information: 44.30863108415906
ML: -6482.774140181071 Information: 44.30863108345329
ML: -6482.774140181071 Information: 44.30863108319318
ML: -6482.774140181071 Information: 44.30863108309768
ML: -6482.774140181071 Information: 44.30863108306221
ML: -6482.774140181071 Information: 44.30863108304948
ML: -6482.774140181071 Information: 44.30863108304493
ML: -6482.774140181071 Information: 44.30863108304311
ML: -6482.774140181071 Information: 44.3086310830422
ML: -6482.774140181071 Information: 44.3086310830422
89<=10000000000&& (89<2.0*44.3086310830422*1||Math.abs(-6482.774140181071 - -6482.774140181071)/Math.abs(-6482.774140181071) > 1.0
Finished in 89 steps!
Marginal likelihood: -6482.258778300394 (bootstrap SD=6.3740821217718935)
Marginal likelihood: -6481.429556962358 (subsample SD=7.717558298097089)
Marginal likelihood: -6482.785768124018(6.833409275448446)
Information: 44.3086310830422
SD: 6.65647287105132

Operator                                              Tuning    #accept    #reject      Pr(m)  Pr(acc|m)
ScaleOperator(YuleBirthRateScaler.t:primates)        0.75000      13657       4192    0.04000    0.76514 Try setting scaleFactor to about 0.562
ScaleOperator(YuleModelTreeScaler.t:primates)        0.50000       2467      15756    0.04000    0.13538 
ScaleOperator(YuleModelTreeRootScaler.t:primates)    0.50000       1610      16457    0.04000    0.08911 Try setting scaleFactor to about 0.707
Uniform(YuleModelUniformOperator.t:primates)               -      59013     121435    0.40000    0.32704 
SubtreeSlide(YuleModelSubtreeSlide.t:primates)       1.00000       1318      88184    0.20000    0.01473 Try decreasing size to about 0.5
Exchange(YuleModelNarrow.t:primates)                       -      21871      68001    0.20000    0.24336 
Exchange(YuleModelWide.t:primates)                         -       1004      17075    0.04000    0.05553 
WilsonBalding(YuleModelWilsonBalding.t:primates)           -        885      17075    0.04000    0.04928 

     Tuning: The value of the operator's tuning parameter, or '-' if the operator can't be optimized.
    #accept: The total number of times a proposal by this operator has been accepted.
    #reject: The total number of times a proposal by this operator has been rejected.
      Pr(m): The probability this operator is chosen in a step of the MCMC (i.e. the normalized weight).
  Pr(acc|m): The acceptance probability (#accept as a fraction of the total proposals for this operator).


Total calculation time: 17.165 seconds
End likelihood: 11.219717578767929
Producing posterior samples

Marginal likelihood: -6481.810657013448 sqrt(H/N)=(6.489167542270841)=?=SD=(6.671709390277811) Information: 42.109295391661384
Max ESS: 7.905853498440624


Processing 89 trees from file.
Log file written to /home/richel/ns_2.posterior.trees
Done!

Marginal likelihood: -6482.047859072695 sqrt(H/N)=(6.504666448161965)=?=SD=(6.820779663704277) Information: 42.310685601843986
Max ESS: 7.744327344657105


Log file written to /home/richel/ns_2.posterior.txt
Done!

Marginal likelihood: -6482.01338247287 sqrt(H/N)=(6.488946466034643)=?=SD=(6.3878109899164235) Information: 42.10642623906348
Max ESS: 7.742249401124373


Log file written to /home/richel/ns_2.posterior.txt
Done!
This was referenced Jan 29, 2020
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richelbilderbeek commented Jan 29, 2020 •
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If the answer is: 'increase the sub-chain length first': I am already looking into this

For a sub-chain length of 500,000, there still is no effect...

@richelbilderbeek richelbilderbeek mentioned this issue Jan 30, 2020
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@rbouckaert: how is the progress regarding this Issue? It would relief me to know the epsilon works just fine and the error is on my side 👍

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