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PyMC

PyMC (formerly known as PyMC3) is a probabilistic programming library for Python. It can be used for Bayesian statistical modeling and probabilistic machine learning.

Products, Overview & Inference engines

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Research this topic

Explore the main themes, entities and connections around PyMC. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

License
Apache License, Version 2.0
Operating system
Unix-like, Mac OS X, Microsoft Windows
Original author
PyMC Development Team
Other names
PyMC2, PyMC3
Platform
Intel x86 – 32-bit, x64
Release
April 6, 2012 (2012-04-06)

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Inference engines

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

PyMC

Nodes25
Edges24
Triples31
Avg. degree1.92
Density0.08
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

PyMC

Top relations

related to Inference engines · 11
PyMC → Bayesian, Black-box Variational Inference, Hamiltonian Monte Carlo, Hastings, Markov, MCMC, MCMC-based, Monte Carlo, No-U-Turn, NUTS, PyMC's
see also · 5
PyMC → ArviZ, Bayesian, PyMCList, Python, Stan
related to External links · 3
PyMC → Git, GitHubPyTensor, Python
License · 1
PyMC → Apache License, Version 2.0
Operating system · 1
PyMC → Unix-like, Mac OS X, Microsoft Windows
Original author · 1
PyMC → PyMC Development Team
Other names · 1
PyMC → PyMC2, PyMC3
Platform · 1
PyMC → Intel x86 – 32-bit, x64
Release · 1
PyMC → April 6, 2012 (2012-04-06)
Repository · 1
PyMC → https://github.com/pymc-devs/pymc

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

python probabilistic used inference statistical library version development team pytensor bayesian programming theano monte carlo variational algorithms mathematical previous relies

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
PyMCLicenseApache License, Version 2.01.00infobox
PyMCOperating systemUnix-like, Mac OS X, Microsoft Windows1.00infobox
PyMCOriginal authorPyMC Development Team1.00infobox
PyMCOther namesPyMC2, PyMC31.00infobox
PyMCPlatformIntel x86 – 32-bit, x641.00infobox
PyMCReleaseApril 6, 2012 (2012-04-06)1.00infobox
PyMCRepositoryhttps://github.com/pymc-devs/pymc1.00infobox
PyMCStable release6.3.1 / 16 August 2026; 8 days ago (16 August 2026)1.00infobox
PyMCTypeStatistical package1.00infobox
PyMCWebsitewww.pymc.io1.00infobox
PyMCWritten inPython1.00infobox
PyMCis aopen source project0.90text

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.