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

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models.

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• 1 It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models.
• 2 PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU.
• 3 Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs).

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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