PyMC
Probabilistic modeling and Bayesian inference with automatic sampling methods.
External resource. No execution or independent verification is claimed here.
Do posterior samples from a simple conjugate model agree with its analytic posterior moments and quantiles?
Derive the posterior independently, freeze priors, data and sampling settings, and report all-chain diagnostics and Monte Carlo uncertainty.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.
Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies PyMC with the concise collection-authored summary “Probabilistic modeling and Bayesian inference with automatic sampling methods.” and points to the public upstream repository https://github.com/pymc-devs/pymc. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code primary license · Apache-2.0
The root license contains the project's Apache version 2.0 terms and an explicit PyMC copyright/grant notice. License text: LICENSE at commit 7d04e44998f627f351039b446f88ae1675ce724b; no repository-wide clearance is claimed.
Inspect the license evidence ↗code additional component notice · MIT
The root license also includes an MIT notice for aesara-devs material. The file-to-component mapping is not established by this observation. License text: LICENSE at commit 7d04e44998f627f351039b446f88ae1675ce724b; no repository-wide clearance is claimed.
Inspect the license evidence ↗Before copying source material
- Bundled datasets, examples, submodules, vendored code, and dependency licenses have not been audited; the observed top-level license does not clear all of them.
- README and documentation rights were not independently resolved from the main code license; this catalog only links and describes.
- The root contains both Apache-2.0 and an MIT component notice. Identify the exact component/file mapping when preparing copied artifacts.