PyMC: a modern, and comprehensive probabilistic programming framework in Python

• Published in: PeerJ. Computer science Vol. 9; p. e1516
• Main Authors: Abril-Pla, Oriol, Andreani, Virgile, Carroll, Colin, Dong, Larry, Fonnesbeck, Christopher J., Kochurov, Maxim, Kumar, Ravin, Lao, Junpeng, Luhmann, Christian C., Martin, Osvaldo A., Osthege, Michael, Vieira, Ricardo, Wiecki, Thomas, Zinkov, Robert
• Format: Journal Article
• Language: English
• Published: San Diego, USA PeerJ. Ltd 01.09.2023; PeerJ Inc
• Subjects: Bayesian statistics; Data Science; Differential equations; Markov chain Monte Carlo; Probabilistic programming; Programming Languages; Python; Scientific Computing and Simulation; Statistical modeling

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowin...