aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python
| Autor: | Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin |
|---|---|
| Přispěvatelé: | Sorbonne Université (SU), DECISION, LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Ducamp, Gaspard |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
python Probabilistic Graphical Models [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] Bayesian Networks [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS] c++ [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | HAL |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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