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PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning 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 getting started guide <http://docs.pymc.io/notebooks/getting_started>, or interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc3/master?filepath=%2Fdocs%2Fsource%2Fnotebooks> using Binder! For questions on PyMC3, head on over to our PyMC Discourse <https://discourse.pymc.io/>__ forum.

The future of PyMC3 & Theano

There have been many questions and uncertainty around the future of PyMC3 since Theano stopped getting developed by the original authors, and we started experiments with a PyMC version based on tensorflow probability.

Since then many things changed and we are happy to announce that PyMC3 will continue to rely on Theano, or rather its successors Theano-PyMC (pymc3 <4) and Aesara (pymc3 >=4). Check out https://github.com/aesara-devs/aesara__) and specifically the latest developments on the PyMC3 main branch https://github.com/pymc-devs/pymc3/`.

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 Theano-PyMC <https://theano-pymc.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 <http://docs.pymc.io/notebooks/api_quickstart>__
• The PyMC3 tutorial <http://docs.pymc.io/notebooks/getting_started>__
• PyMC3 examples <https://docs.pymc.io/nb_examples/index.html>__ and the API reference <http://docs.pymc.io/api>__

Learn Bayesian statistics with a book together with PyMC3:

• 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.
• PyMC3 port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/aloctavodia/Doing_bayesian_data_analysis>__ as well as the second edition <https://github.com/JWarmenhoven/DBDA-python>__: Principled introduction to Bayesian data analysis.
• PyMC3 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>__
• PyMC3 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).

PyMC3 talks

There are also several talks on PyMC3 which are gathered in this YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>__ and as part of PyMCon 2020 <https://discourse.pymc.io/c/pymcon/2020talks/15>__

Installation

To install PyMC3 on your system, follow the instructions on the appropriate installation guide:

• Installing PyMC3 on MacOS <https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(MacOS)>__
• Installing PyMC3 on Linux <https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(Linux)>__
• Installing PyMC3 on Windows <https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(Windows)>__

Citing PyMC3

Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 DOI: 10.7717/peerj-cs.55 <https://doi.org/10.7717/peerj-cs.55>__.

Contact

We are using discourse.pymc.io <https://discourse.pymc.io/>__ as our main communication channel. You can also follow us on Twitter @pymc_devs <https://twitter.com/pymc_devs>__ for updates and other announcements.

To ask a question regarding modeling or usage of PyMC3 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>.

To report an issue with PyMC3 please use the issue tracker <https://github.com/pymc-devs/pymc3/issues>__.

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

License

Apache License, Version 2.0 <https://github.com/pymc-devs/pymc3/blob/master/LICENSE>__

Software using PyMC3

• Exoplanet <https://github.com/dfm/exoplanet>__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
• Bambi <https://github.com/bambinos/bambi>__: BAyesian Model-Building Interface (BAMBI) in Python.
• pymc3_models <https://github.com/parsing-science/pymc3_models>__: Custom PyMC3 models built on top of the scikit-learn API.
• PMProphet <https://github.com/luke14free/pm-prophet>__: PyMC3 port of Facebook's Prophet model for timeseries modeling
• webmc3 <https://github.com/AustinRochford/webmc3>__: A web interface for exploring PyMC3 traces
• sampled <https://github.com/ColCarroll/sampled>__: Decorator for PyMC3 models.
• NiPyMC <https://github.com/PsychoinformaticsLab/nipymc>__: Bayesian mixed-effects modeling of fMRI data in Python.
• beat <https://github.com/hvasbath/beat>__: Bayesian Earthquake Analysis Tool.
• pymc-learn <https://github.com/pymc-learn/pymc-learn>__: Custom PyMC models built on top of pymc3_models/scikit-learn API
• fenics-pymc3 <https://github.com/IvanYashchuk/fenics-pymc3>__: Differentiable interface to FEniCS, a library for solving partial differential equations.
• cell2location <https://github.com/BayraktarLab/cell2location>__: Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics.

Please contact us if your software is not listed here.

Papers citing PyMC3

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/pymc3/graphs/contributors>. Also read our Code of Conduct <https://github.com/pymc-devs/pymc3/blob/master/CODE_OF_CONDUCT.md> guidelines for a better contributing experience.

Support

PyMC3 is a non-profit project under NumFOCUS umbrella. If you want to support PyMC3 financially, you can donate here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>__.

PyMC for enterprise

PyMC is now available as part of the Tidelift Subscription!

Tidelift is working with PyMC and the maintainers of thousands of other open source projects to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while contributing financially to PyMC -- making it even more robust, reliable and, let's face it, amazing!

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