| PyMC | |
|---|---|
 | |
| Other names | PyMC2, PyMC3 |
| Original author(s) | PyMC Development Team |
| Initial release | May 4, 2013 |
| Stable release | |
| 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 library for Python. It can be used for Bayesian statistical modeling and probabilistic machine learning.
Contents
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 has been 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]