Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409932 | Neurocomputing | 2014 | 8 Pages |
Abstract
Our work aims at developing or expliciting bridges between Bayesian networks (BNs) and Natural Exponential Families, by proposing discrete exponential Bayesian networks as a generalization of usual discrete ones. We introduce a family of prior distributions which generalizes the Dirichlet prior applied on discrete Bayesian networks, and then we determine the overall posterior distribution. Subsequently, we develop the Bayesian estimators of the parameters, and a new score function that extends the Bayesian Dirichlet score for BN structure learning. Our goal is to determine empirically in which contexts some of our discrete exponential BNs (Poisson deBNs) can be an effective alternative to usual BNs for density estimation.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Aida Jarraya, Philippe Leray, Afif Masmoudi,