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Given the pymc 4.0 release notes (published June 2022) describe a new Jax backend, and the copyright date of the SunODE docs is 2020 vs 2023 for the Diffrax examples I linked, is it reasonable to say that integration of the SunDials library with Pymc is at the very least tabled for now, and those generally looking to solve ODE's with pymc should use learn to use diffrax? Thanks
The text was updated successfully, but these errors were encountered:
I read https://www.pymc.io/projects/examples/en/latest/ode_models/ODE_Lotka_Volterra_multiple_ways.html and noticed the 'Bayesian Inference with Gradients' section the recommends to use sunode or diffrax to perform inference with gradients. The article linked for diffrax in that section (https://www.pymc-labs.com/blog-posts/jax-functions-in-pymc-3-quick-examples/) lists @aseyboldt (one of the authors of the Sunode library, and the sunode docs author) as the author alongside Ricardo Vieira with a 2023 publication date.
Given the pymc 4.0 release notes (published June 2022) describe a new Jax backend, and the copyright date of the SunODE docs is 2020 vs 2023 for the Diffrax examples I linked, is it reasonable to say that integration of the SunDials library with Pymc is at the very least tabled for now, and those generally looking to solve ODE's with pymc should use learn to use diffrax? Thanks
The text was updated successfully, but these errors were encountered: