Graphical Reasoning with Bayesian Networks

Abstract

Nowadays, Bayesian networks are seen by many researchers as standard tools for reasoning with uncertainty. Despite the fact that Bayesian networks are graphical representations, representing dependence and independence information, normally the emphasis of the visualisation of the reasoning process is on showing changes in the associated marginal probability distributions due to entering observations, rather than on changes in the associated graph structure. In this paper, we argue that it is possible and relevant to look at Bayesian network reasoning as reasoning with a graph structure, depicting changes in the dependence and independence information. We propose a new method that is able to modify the graphical part of a Bayesian network to bring it in accordance with available observations. In this way, Bayesian network reasoning is seen as reasoning about changing dependences and independences as reflected by changes in the graph structure.