Discrete exponential bayesian networks structure learning for density estimation - LINA-DUKE
Communication Dans Un Congrès Année : 2012

Discrete exponential bayesian networks structure learning for density estimation

Résumé

Our work aims at developing or expliciting bridges between Bayesian Networks and Natural Exponential Families, by proposing discrete exponential Bayesian networks as a generalization of usual discrete ones. In this paper, we illustrate the use of discrete exponential Bayesian networks for Bayesian structure learning and density estimation. Our goal is to empirically determine in which contexts these models can be a good alternative to usual Bayesian networks for density estimation.
Fichier principal
Vignette du fichier
Aida_DeBN-6p.pdf (106.29 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00691834 , version 1 (17-04-2020)

Identifiants

Citer

Aida Jarraya, Philippe Leray, Afif Masmoudi. Discrete exponential bayesian networks structure learning for density estimation. International Conference on Intelligent Computing, 2012, Huangshan, China. pp.?-?, ⟨10.1007/978-3-642-31837-5_21⟩. ⟨hal-00691834⟩
258 Consultations
136 Téléchargements

Altmetric

Partager

More