aGrUM/pyAgrum : a Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python']Python

被引：0

作者：

Ducamp, Gaspard ^{[1
]}

Gonzales, Christophe ^{[2
]}

Wuillemin, Pierre-Henri ^{[1
]}

机构：

[1] Sorbonne Univ, LIP6, 4 Pl Jussieu, F-75005 Paris, France

[2] Univ Toulon & Var, Aix Marseille Univ, CNRS, LIS, Marseille, France

来源：

INTERNATIONAL CONFERENCE ON PROBABILISTIC GRAPHICAL MODELS, VOL 138 | 2020年 / 138卷

关键词：

Bayesian Networks; Probabilistic Graphical Models; c plus; !text type='python']python[!/text;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：4

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