Learning NAT-Modeled Bayesian Network Structures with Bayesian Approach

Abstract

We study Bayesian approach for learning structures of Bayesian networks (BNs) with local models. The local structures we focus on are Non-impeding noisy-AND Tree (NAT) models due to their multiple merits. We extend meta-nets to allow encoding of prior knowledge on NAT local structures and parameters. From the extended meta-nets, we develop a Bayesian Dirichlet (BD) scoring function for evaluating alternative NAT-modeled BN structures. A heuristic algorithm is presented for searching through the structure space that is significantly more complex than that of BN structures without local models. We experimentally demonstrate learning of NAT-modeled BNs, whose inference produces sufficiently accurate posterior marginals and is significantly more efficient.