开源软件名称（OpenSource Name）：

pymc-devs/pymc

开源软件地址(OpenSource Url)：

https://github.com/pymc-devs/pymc

开源编程语言(OpenSource Language)：

开源软件介绍(OpenSource Introduction)：

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

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).

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:

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

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.

License

Apache License, Version 2.0

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.