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.
Cite
Text
Ducamp et al. "aGrUM/pyAgrum : A Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python." Proceedings of pgm 2020, 2020.Markdown
[Ducamp et al. "aGrUM/pyAgrum : A Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python." Proceedings of pgm 2020, 2020.](https://mlanthology.org/pgm/2020/ducamp2020pgm-agrum/)BibTeX
@inproceedings{ducamp2020pgm-agrum,
title = {{aGrUM/pyAgrum : A Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python}},
author = {Ducamp, Gaspard and Gonzales, Christophe and Wuillemin, Pierre-Henri},
booktitle = {Proceedings of pgm 2020},
year = {2020},
volume = {138},
url = {https://mlanthology.org/pgm/2020/ducamp2020pgm-agrum/}
}