Discrete exponential bayesian networks structure learning for density estimation

Aida Jarraya (1, 2) , Philippe Leray (1) , Afif Masmoudi (3)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 Laboratoire de Probabilité et Statistiques
3 laboratoire de probabilités et statistique

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.

Domains: Machine Learning [cs.LG]

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⟩