Project: pymc

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor

Project Details

Latest version: 5.10.3
Home Page: http://github.com/pymc-devs/pymc
PyPI Page: https://pypi.org/project/pymc/

Project Popularity

PageRank: 0.007791358914336301
Number of downloads: 139364

.. image:: https://cdn.rawgit.com/pymc-devs/pymc/main/docs/logos/svg/PyMC_banner.svg :height: 100px :alt: PyMC logo :align: center

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Check out the PyMC overview <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>, or one of the many examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>! For questions on PyMC, head on over to our PyMC Discourse <https://discourse.pymc.io/>__ forum.

Features

Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal('x',0,1)
Powerful sampling algorithms, such as the No U-Turn Sampler <http://www.jmlr.org/papers/v15/hoffman14a.html>__, allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
Variational inference: ADVI <http://www.jmlr.org/papers/v18/16-107.html>__ for fast approximate posterior estimation as well as mini-batch ADVI for large data sets.
Relies on PyTensor <https://pytensor.readthedocs.io/en/latest/>__ which provides:
- Computation optimization and dynamic C or JAX compilation
- NumPy broadcasting and advanced indexing
- Linear algebra operators
- Simple extensibility
Transparent support for missing value imputation

Getting started

If you already know about Bayesian statistics:

API quickstart guide <https://www.pymc.io/projects/examples/en/latest/howto/api_quickstart.html>__
The PyMC tutorial <https://docs.pymc.io/en/latest/learn/core_notebooks/pymc_overview.html>__
PyMC examples <https://www.pymc.io/projects/examples/en/latest/gallery.html>__ and the API reference <https://docs.pymc.io/en/stable/api.html>__

Learn Bayesian statistics with a book together with PyMC

Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>__: Fantastic book with many applied code examples.
PyMC port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/cluhmann/DBDA-python>__ as well as the first edition <https://github.com/aloctavodia/Doing_bayesian_data_analysis>__.
PyMC port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath <https://github.com/pymc-devs/resources/tree/master/Rethinking>__
PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/pymc-devs/resources/tree/master/BCM>__: Focused on using Bayesian statistics in cognitive modeling.
Bayesian Analysis with Python <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition>__ (second edition) by Osvaldo Martin: Great introductory book. (code <https://github.com/aloctavodia/BAP>__ and errata).

Audio & Video

Here is a YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>__ gathering several talks on PyMC.
You can also find all the talks given at PyMCon 2020 here <https://discourse.pymc.io/c/pymcon/2020talks/15>__.
The "Learning Bayesian Statistics" podcast <https://www.learnbayesstats.com/>__ helps you discover and stay up-to-date with the vast Bayesian community. Bonus: it's hosted by Alex Andorra, one of the PyMC core devs!

Installation

To install PyMC on your system, follow the instructions on the installation guide <https://www.pymc.io/projects/docs/en/latest/installation.html>__.

Citing PyMC

Please choose from the following:

|DOIpaper| PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python, Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R. (2023)
|DOIzenodo| A DOI for all versions.
DOIs for specific versions are shown on Zenodo and under Releases <https://github.com/pymc-devs/pymc/releases>_

.. |DOIpaper| image:: https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.1516-blue :target: https://doi.org/10.7717/peerj-cs.1516 .. |DOIzenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg :target: https://doi.org/10.5281/zenodo.4603970

Contact

We are using discourse.pymc.io <https://discourse.pymc.io/>__ as our main communication channel.

To ask a question regarding modeling or usage of PyMC we encourage posting to our Discourse forum under the “Questions” Category <https://discourse.pymc.io/c/questions>. You can also suggest feature in the “Development” Category <https://discourse.pymc.io/c/development>.

You can also follow us on these social media platforms for updates and other announcements:

LinkedIn @pymc <https://www.linkedin.com/company/pymc/>__
YouTube @PyMCDevelopers <https://www.youtube.com/c/PyMCDevelopers>__
Twitter @pymc_devs <https://twitter.com/pymc_devs>__
Mastodon @pymc@bayes.club <https://bayes.club/@pymc>__

To report an issue with PyMC please use the issue tracker <https://github.com/pymc-devs/pymc/issues>__.

Finally, if you need to get in touch for non-technical information about the project, send us an e-mail <info@pymc-devs.org>__.

License

Apache License, Version 2.0 <https://github.com/pymc-devs/pymc/blob/main/LICENSE>__

Software using PyMC

General purpose

Bambi <https://github.com/bambinos/bambi>__: BAyesian Model-Building Interface (BAMBI) in Python.
calibr8 <https://calibr8.readthedocs.io>__: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.
gumbi <https://github.com/JohnGoertz/Gumbi>__: A high-level interface for building GP models.
SunODE <https://github.com/aseyboldt/sunode>__: Fast ODE solver, much faster than the one that comes with PyMC.
pymc-learn <https://github.com/pymc-learn/pymc-learn>__: Custom PyMC models built on top of pymc3_models/scikit-learn API

Domain specific

Exoplanet <https://github.com/dfm/exoplanet>__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
beat <https://github.com/hvasbath/beat>__: Bayesian Earthquake Analysis Tool.
CausalPy <https://github.com/pymc-labs/CausalPy>__: A package focussing on causal inference in quasi-experimental settings.

Please contact us if your software is not listed here.

Papers citing PyMC

See Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>__ for a continuously updated list.

Contributors

See the GitHub contributor page <https://github.com/pymc-devs/pymc/graphs/contributors>. Also read our Code of Conduct <https://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md> guidelines for a better contributing experience.

Support

PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>__.

Professional Consulting Support

You can get professional consulting support from PyMC Labs <https://www.pymc-labs.io>__.