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

Project description

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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 Aesara 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:

Learn Bayesian statistics with a book together with PyMC

Audio & Video

Installation

To install PyMC on your system, follow the instructions on the installation guide.

Citing PyMC

Please choose from the following:

  • DOIpaper Probabilistic programming in Python using PyMC3, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)
  • DOIzenodo A DOI for all versions.
  • DOIs for specific versions are shown on Zenodo and under Releases

Contact

We are using discourse.pymc.io as our main communication channel. You can also follow us on Twitter @pymc_devs for updates and other announcements.

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.

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.

Software using PyMC

General purpose

  • Bambi: BAyesian Model-Building Interface (BAMBI) in Python.
  • 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
  • fenics-pymc3: Differentiable interface to FEniCS, a library for solving partial differential equations.

Domain specific

  • Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
  • NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.
  • beat: Bayesian Earthquake Analysis Tool.
  • 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 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

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