| Original author(s) | PyMC Development Team |
|---|---|
| Initial release | May 4, 2013 |
| Stable release | 5.10.3[1] 
/ 21 December 2023 |
| Repository | https://github.com/pymc-devs/pymc |
| Written in | Python |
| Operating system | Unix-like, Mac OS X, Microsoft Windows |
| Platform | Intel x86 – 32-bit, x64 |
| Type | Statistical package |
| License | Apache License, Version 2.0 |
| Website | www |
PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. It can be used for Bayesian statistical modeling and probabilistic machine learning.
PyMC performs inference based on advanced Markov chain Monte Carlo and/or variational fitting algorithms.[2] [3][4][5][6] It is a rewrite from scratch of the previous version of the PyMC software.[7] Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC relies on PyTensor, a Python library that allows defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. From version 3.8 PyMC relies on ArviZ to handle plotting, diagnostics, and statistical checks. PyMC and Stan are the two most popular probabilistic programming tools.[8] PyMC is an open source project, developed by the community and fiscally sponsored by NumFOCUS.[9]
PyMC has been used to solve inference problems in several scientific domains, including astronomy,[10][11] epidemiology,[12][13] molecular biology,[14] crystallography,[15][16] chemistry,[17] ecology[18][19] and psychology.[20] Previous versions of PyMC were also used widely, for example in climate science,[21] public health,[22] neuroscience,[23] and parasitology.[24][25]
After Theano announced plans to discontinue development in 2017,[26] the PyMC team evaluated TensorFlow Probability as a computational backend,[27] but decided in 2020 to fork Theano under the name Aesara.[28] Large parts of the Theano codebase have been refactored and compilation through JAX[29] and Numba were added. The PyMC team has released the revised computational backend under the name PyTensor and continues the development of PyMC.[30]