A Framework for Bayesian Network Mapping

Abstract

This research is motivated by the need to support inference across multiple intelligence systems involving uncertainty. Our objective is to develop a theoretical framework and related inference methods to map semantically similar variables between separate Bayesian networks in a principled way. The work is to be conducted in two steps. In the first step, we investigate the problem of formalizing the mapping between variables in two separate BNs with different semantics and distributions as pair-wise linkages. In the second step, we aim to justify the mapping between networks as a set of selected variable linkages, and then conduct inference along it. At present, a Bayesian network (BN) is used primarily as a standalone system. When the problem scope is large, a large network slows down inference process and is difficult to