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Bayesian Modeling and Probabilistic Programming in Python
https://github.com/pymc-devs/pymc

bayesian-inference mcmc probabilistic-programming pytensor python statistical-analysis variational-inference

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Bayesian Modeling and Probabilistic Programming in Python

Lists

README 

        
.. image:: https://cdn.rawgit.com/pymc-devs/pymc/main/docs/logos/svg/PyMC_banner.svg

    :height: 100px

    :alt: PyMC logo

    :align: center



|Build Status| |Coverage| |NumFOCUS_badge| |Binder| |Dockerhub| |DOIzenodo|



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 `__,  or

one of `the many examples `__!

For questions on PyMC, head on over to our `PyMC Discourse `__ 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 `__, allow complex models

   with thousands of parameters with little specialized knowledge of

   fitting algorithms.

-  **Variational inference**: `ADVI `__

   for fast approximate posterior estimation as well as mini-batch ADVI

   for large data sets.

-  Relies on `PyTensor `__ 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 `__

-  The `PyMC tutorial `__

-  `PyMC examples `__ and the `API reference `__



Learn Bayesian statistics with a book together with PyMC

--------------------------------------------------------



-  `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 `__ as well as the `first edition `__.

-  `PyMC port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath `__

-  `PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers `__: Focused on using Bayesian statistics in cognitive modeling.

-  `Bayesian Analysis with Python  `__ (second edition) by Osvaldo Martin: Great introductory book. (`code `__ and errata).



Audio & Video

-------------



- Here is a `YouTube playlist `__ gathering several talks on PyMC.

- You can also find all the talks given at **PyMCon 2020** `here `__.

- The `"Learning Bayesian Statistics" podcast `__ 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 `__.



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 `_



.. |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 `__ 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 `__. You can also suggest feature in the `“Development” Category `__.



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



- `LinkedIn @pymc `__

- `YouTube @PyMCDevelopers `__

- `Twitter @pymc_devs `__

- `Mastodon @[email protected] `__



To report an issue with PyMC please use the `issue tracker `__.



Finally, if you need to get in touch for non-technical information about the project, `send us an e-mail `__.



License

=======



`Apache License, Version

2.0 `__



Software using PyMC

===================



General purpose

---------------



- `Bambi `__: BAyesian Model-Building Interface (BAMBI) in Python.

- `calibr8 `__: A toolbox for constructing detailed observation models to be used as likelihoods in PyMC.

- `gumbi `__: A high-level interface for building GP models.

- `SunODE `__: Fast ODE solver, much faster than the one that comes with PyMC.

- `pymc-learn `__: Custom PyMC models built on top of pymc3_models/scikit-learn API



Domain specific

---------------



- `Exoplanet `__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.

- `beat `__: Bayesian Earthquake Analysis Tool.

- `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 `__ for a continuously updated list.



Contributors

============



See the `GitHub contributor

page `__. Also read our `Code of Conduct `__ 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 `__.



Professional Consulting Support

===============================



You can get professional consulting support from `PyMC Labs `__.



Sponsors

========



|NumFOCUS|



|PyMCLabs|



|Mistplay|



|ODSC|



.. |Binder| image:: https://mybinder.org/badge_logo.svg

   :target: https://mybinder.org/v2/gh/pymc-devs/pymc/main?filepath=%2Fdocs%2Fsource%2Fnotebooks

.. |Build Status| image:: https://github.com/pymc-devs/pymc/workflows/pytest/badge.svg

   :target: https://github.com/pymc-devs/pymc/actions

.. |Coverage| image:: https://codecov.io/gh/pymc-devs/pymc/branch/main/graph/badge.svg

   :target: https://codecov.io/gh/pymc-devs/pymc

.. |Dockerhub| image:: https://img.shields.io/docker/automated/pymc/pymc.svg

   :target: https://hub.docker.com/r/pymc/pymc

.. |NumFOCUS_badge| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A

   :target: http://www.numfocus.org/

.. |NumFOCUS| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_numfocus.png?raw=true

   :target: http://www.numfocus.org/

.. |PyMCLabs| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_pymc_labs.png?raw=true

   :target: https://pymc-labs.io

.. |Mistplay| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/sponsor_mistplay.png?raw=true

   :target: https://www.mistplay.com/

.. |ODSC| image:: https://github.com/pymc-devs/brand/blob/main/sponsors/sponsor_logos/odsc/sponsor_odsc.png?raw=true

   :target: https://odsc.com/california/?utm_source=pymc&utm_medium=referral