Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets

Zu Whan Kim, Ramakant Nevatia

Abstract: We introduce an expandable Bayesian network (EBN) to handle the combination of diverse multiple homogeneous evidence sets. An EBN is an augmented Bayesian network which instantiates its structure at runtime according to the structure of input. We show an application of an EBN for a multi-view 3-D object description problem in computer vision. The experiments show that the proposed method gives reasonable performance even for an unlearned structure of input data.