PyMC3 Python probabilistic modeling and gradient-based sampling

What changed

Presents a Python model-context interface using symbolic computation for Bayesian model specification and gradient-based HMC/NUTS sampling.

Scientific scope

Presents a Python model-context interface using symbolic computation for Bayesian model specification and gradient-based HMC/NUTS sampling. The report concerns PyMC3 rather than the latest PyMC release. NUTS, Hamiltonian Monte Carlo and symbolic automatic differentiation are inherited methods. A convenient model specification does not establish convergence or posterior validity.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The report concerns PyMC3 rather than the latest PyMC release. NUTS, Hamiltonian Monte Carlo and symbolic automatic differentiation are inherited methods. A convenient model specification does not establish convergence or posterior validity.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Probabilistic Programming in Python using PyMC

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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