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.
- 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
If you already know about Bayesian statistics:
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 second edition: Principled introduction to Bayesian data analysis.
- 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).
To install PyMC on your system, follow the instructions on the appropriate installation guide:
Please choose from the following:
- Probabilistic programming in Python using PyMC3, Salvatier J., Wiecki T.V., Fonnesbeck C. (2016)
- A DOI for all versions.
- DOIs for specific versions are shown on Zenodo and under Releases
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
- Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.
- Bambi: BAyesian Model-Building Interface (BAMBI) in Python.
- pymc3_models: Custom PyMC models built on top of the scikit-learn API.
- PMProphet: PyMC port of Facebook's Prophet model for timeseries modeling
- webmc3: A web interface for exploring PyMC traces
- sampled: Decorator for PyMC models.
- NiPyMC: Bayesian mixed-effects modeling of fMRI data in Python.
- beat: Bayesian Earthquake Analysis Tool.
- 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.
- 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.
PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here.
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!
You can also get professional consulting support from PyMC Labs.