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

Gaspard Ducamp; Christophe Gonzales; Pierre-Henri Wuillemin

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

Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin

Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138, 2020.

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 this Paper

BibTeX


@InProceedings{pmlr-v138-ducamp20a,
  title = 	 {aGrUM/pyAgrum : a toolbox to build models and algorithms 
 for Probabilistic Graphical Models in Python},
  author =       {Ducamp, Gaspard and Gonzales, Christophe and Wuillemin, pages = {609-612}, Pierre-Henri},
  booktitle = 	 {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
  year = 	 {2020},
  editor = 	 {Jaeger, Manfred and Nielsen, Thomas Dyhre},
  volume = 	 {138},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--25 Sep},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v138/ducamp20a/ducamp20a.pdf},
  url = 	 {https://proceedings.mlr.press/v138/ducamp20a.html},
  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.}
}

Endnote

%0 Conference Paper
%T aGrUM/pyAgrum : a toolbox to build models and algorithms 
 for Probabilistic Graphical Models in Python
%A Gaspard Ducamp
%A Christophe Gonzales
%A Pierre-Henri Wuillemin
%B Proceedings of the 10th International Conference on Probabilistic Graphical Models
%C Proceedings of Machine Learning Research
%D 2020
%E Manfred Jaeger
%E Thomas Dyhre Nielsen	
%F pmlr-v138-ducamp20a
%I PMLR
%U https://proceedings.mlr.press/v138/ducamp20a.html
%V 138
%X 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.

APA


Ducamp, G., Gonzales, C. & Wuillemin, P.. (2020). aGrUM/pyAgrum : a toolbox to build models and algorithms 
 for Probabilistic Graphical Models in Python. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138 Available from https://proceedings.mlr.press/v138/ducamp20a.html.

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