aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python

Gaspard Ducamp (1, 2) , Christophe Gonzales (3, 4) , Pierre-Henri Wuillemin (1, 2)

1 SU - Sorbonne Université
2 DECISION
3 AMU - Aix Marseille Université
4 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

This paper presents the aGrUM framework, a LGPL C++ library providing state-of-the-art implementations of graphical models for decision making, including Bayesian Networks, Markov Networks (Markov random fields), Influence Diagrams, Credal Networks, Probabilistic Relational Models. The framework also contains a wrapper, pyAgrum for exploiting aGrUM in Python. This framework is the result of an ongoing effort to build an efficient and well maintained open source cross-platform software, running on Linux, MacOS X and Windows, for dealing with graphical models and for providing essential components to build new algorithms for graphical models.

Mots clés: Bayesian Networks; Probabilistic Graphical Models; c++; python

Domaines: Algorithme et structure de données [cs.DS]; Intelligence artificielle [cs.AI]

Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin. aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python. 10th International Conference on Probabilistic Graphical Models, Sep 2020, Skørping, Denmark. pp.609-612. ⟨hal-03135721⟩