PyMC

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PyMC
Other namesPyMC2, PyMC3
Original authorPyMC Development Team
Initial releaseApril 6, 2012 (2012-04-06)
Repositoryhttps://github.com/pymc-devs/pymc
Written inPython
Engine
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    Operating systemUnix-like, Mac OS X, Microsoft Windows
    PlatformIntel x86 – 32-bit, x64
    TypeStatistical package
    License Apache License, Version 2.0
    Websitewww.pymc.io

    PyMC (formerly known as PyMC3) is a probabilistic programming library for 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.[1][2][3][4][5] It is a rewrite from scratch of the previous version of the PyMC software.[6] 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.[7] PyMC is an open source project, developed by the community and has been fiscally sponsored by NumFOCUS.[8]

    PyMC has been used to solve inference problems in several scientific domains, including astronomy,[9][10] epidemiology,[11][12] molecular biology,[13] crystallography,[14][15] chemistry,[16] ecology[17][18] and psychology.[19] Previous versions of PyMC were also used widely, for example in climate science,[20] public health,[21] neuroscience,[22] and parasitology.[23][24]

    After Theano announced plans to discontinue development in 2017,[25] the PyMC team evaluated TensorFlow Probability as a computational backend,[26] but decided in 2020 to fork Theano under the name Aesara.[27] Large parts of the Theano codebase have been refactored and compilation through JAX[28] and Numba were added. The PyMC team has released the revised computational backend under the name PyTensor and continues the development of PyMC.[29]

    Inference engines

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    PyMC implements non-gradient-based and gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference and stochastic, gradient-based variational Bayesian methods for approximate Bayesian inference.

    See also

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    • Stan is a probabilistic programming language for statistical inference written in C++
    • ArviZ a Python library for exploratory analysis of Bayesian models
    • Bambi is a high-level Bayesian model-building interface based on PyMC

    References

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    1. ^ 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) PyMC: a modern, and comprehensive probabilistic programming framework in Python. PeerJ Comput. Sci. 9:e1516 Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
    2. ^ Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
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    Further reading

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    • Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
    [edit | edit source]
    • PyMC website
    • PyMC source, a Git repository hosted on GitHub
    • PyTensor is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
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