Simplifying Learning in Non-repetitive Dynamic Bayesian Networks

Abstract

Dynamic Bayesian networks (DBNs) are increasingly adopted as tools for the modeling of dynamic domains involving uncertainty. Due to their ease of modeling, repetitive DBNs have become the standard. However, repetition does not allow the independence relations to vary over time. Non-repetitive DBNs do allow for modeling time-varying relations, but are hard to apply to dynamic domains.

This paper presents a novel method that facilitates the use of non-repetitive DBNs and simplifies learning DBNs in general. This is achieved by learning disjoint sets of independence relations of separate parts of a DBN, and, subsequently, joining these relations together to obtain the complete set of independence relations of the DBN. Our simplified learning method improves previous methods by removing redundant operations which yields computational savings in the learning process of the network. Experimental results show that the simplified learning method facilitates the use of non-repetitive DNBs and enables us to build them in a seamless fashion.