An Improved Structural EM to Learn Dynamic Bayesian Nets

Cassio Polpo de Campos, Zhi Zeng, Qiang Ji

Abstract: This paper addresses the problem of learning structure of Bayesian and Dynamic Bayesian networks from incomplete data based on the Bayesian Information Criterion. We describe a procedure to map the problem of the dynamic case into a corresponding augmented Bayesian network through the use of structural constraints. Because the algorithm is exact and anytime, it is well suitable for a structural Expectation-Maximization (EM) method where the only source of approximation is due to the EM itself. We show empirically that the use a global maximizer inside the structural EM is computationally feasible and leads to more accurate models.