Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
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.. warning::
This is the legacy version of PyMC3, now renamed to PyMC.
If you are looking for the latest version of PyMC, please visit
PyMC's documentation <https://www.pymc.io/welcome.html>__
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.
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/`.
x ~ N(0,1)
translates to x = Normal('x',0,1)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.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.Theano-PyMC <https://theano-pymc.readthedocs.io/en/latest/>__ which provides:
API quickstart guide <http://docs.pymc.io/notebooks/api_quickstart>__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>__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).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>__
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)>__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>__.
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>__.
Apache License, Version 2.0 <https://github.com/pymc-devs/pymc3/blob/master/LICENSE>__
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 modelingwebmc3 <https://github.com/AustinRochford/webmc3>__: A web interface for exploring PyMC3 tracessampled <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 APIfenics-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.
See Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>__ for a continuously updated list.
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.
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 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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Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
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