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A Python library for Probabilistic Graphical Models

A Python library for Probabilistic Graphical Models

A Python wrapper for aGrUM, offering a user-friendly interface to create, model, learn, and calculate with Bayesian networks and other graphical models.

Get Started Install pyAgrum

pyAgrum

A session with pyAgrum

A Bayesian Network with pyAgrum

An evidence propagation with pyAgrum

Building a Bayesian network with pyAgrum

Dynamic Bayesian network with pyAgrum

Inspecting junction tree with pyAgrum

Comparing Bayesian networks with pyAgrum

Customizing inference with pyAgrum

Do-calculus with pyAgrum

aGrUM/pyAgrum share features

Bayesian network as a tool with pyAgrum

Credal network with pyAgrum

Latest Releases

aGrUM/pyAgrum 3.1.1 released

August 19, 2026

...

aGrUM/pyAgrum 3.1.0 released

August 18, 2026

pyAgrum ...

aGrUM/pyAgrum 3.0.0 released

July 17, 2026

This major release brings three headline changes: the causal module is promoted from pure Python to a first-class C++ module (CM), structure learning is extended with PC, FCI (with PAG output), GreedyThickThinning, and triangle ...

Why pyAgrum?

Features	pyAgrum	pgmpy	bnlearn
Multi-PGM Support	✔	—	—
High-Performance C++ Core	✔	—	—
Causal Inference Tools	✔	✔	—
Influence Diagrams	✔	—	✔
Active Community	✔	✔	✔

Key Tasks

Classification

Build classifiers using Bayesian Networks for accurate predictions.

Prediction

Make probabilistic predictions based on observed evidence.

Causal Analysis

Discover causal relationships in your data with advanced algorithms.

Decision Making

Optimize decisions under uncertainty with Influence Diagrams.

Explanation

Explain model predictions using probabilistic reasoning.

Model selection

Select the best model structure and parameters for your data.

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Install pyAgrum