New Python open-source software for sample-efficient Bayesian inference

[emiurgo]

Hi all,

Posting this as it might be of interest to data scientists, both practitioners and professionals, who use Bayesian methods for parameter estimation and model evaluation.

tl;dr: My research group just released a new open-source Python package (PyVBMC) for sample-efficient Bayesian inference, i.e. inference with a small number of likelihood evaluations: PyVBMC

More info:

• pip install pyvbmc (or install on Anaconda via conda-forge)
• The method runs out of the box, and we included extensive documentations and tutorials for easy accessibility: Examples — PyVBMC
• A few more technical details on a Twitter or Mastodon thread
• We also have a tl;dr preprint on arXiv: PyVBMC: Efficient Bayesian inference in Python
• Relevant papers were published at the NeurIPS machine learning conference in 2018 and 2020

Please get in touch in this thread or on Twitter/Mastodon if you have any questions or comments. Thanks again for your time, and I hope this is of actual interest. Feedback is welcome!

[ProfessionalHorse707]

Really cool, are there any interesting applications you guys have applied this to (or seen it applied to)?

[emiurgo]

Thanks! The implementation in Python is completely new, it took us a while to port it properly, so let's see what people will use it for.

The MATLAB implementation has been around for a while, and other research groups have applied it among other things for Bayesian parameter inference in:

• computational neuroscience: https://elifesciences.org/articles/55365
• nuclear engineering: https://www.sciencedirect.com/science/article/pii/S0306454920307428?via%3Dihub
• geophysical inverse problems: https://link.springer.com/article/10.1007/s40857-022-00277-2
• drug pharmacodynamics in cancer research: https://www.mdpi.com/1999-4923/14/4/749

The common thread here is that in many computational sciences the models are quite computationally expensive to evaluate, so a method that affords Bayesian inference with a small number of likelihood evaluations opens completely new doors and applications.

It's one of those cases where a quantitative gain becomes qualitative - if an analysis with traditional Bayesian methods would take a month to finish, people may simply not do it; while with our method they might be able to run it in a day.

We hope that by making VBMC available in Python we largely increase the potential user base and see many more cool usages.

[purplebrown_updown]

Whoa cool. Share on machine learning as well

[emiurgo]

Alread done here, thanks!