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

Abstract

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.
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Dates and versions

hal-03135721 , version 1 (09-02-2021)

Identifiers

  • HAL Id : hal-03135721 , version 1

Cite

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⟩
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