To help you get started, we've collected the most common ways that pymc is being used within popular public projects.

pymc-devs / pymc3 / pymc / sandbox / test_model_ave.py View on Github

def test(self):

        # Changepoint model
        M1 = Model(model_1)

        # Constant rate model
        M2 = Model(model_2)

        # Exponentially varying rate model
        M3 = Model(model_3)

        # print 'Docstring of model 1:'
        # print model_1.__doc__
        # print 'Docstring of model 2:'
        # print model_2.__doc__
        # print 'Docstring of model 3:'
        # print model_3.__doc__


        posterior = weight([M1,M2,M3],10000)[0]

        # print 'Log posterior probability of changepoint model: ',log(posterior[M1])
        # print 'Log posterior probability of constant rate model: ',log(posterior[M2])

View more ways to use pymc

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

• Bayesian Analysis with Python (third edition) by Osvaldo Martin: Great introductory book.
• 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.

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:

• 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)
• 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.

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 @pymc@bayes.club

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 here and here 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

Thanks to our contributors