REPRESENTING CONDITIONAL INDEPENDENCE RELATIONS BY VALUATION NETWORKS

Abstract

Valuation networks have been proposed as graphical representations of valuation-based systems. The axiomatic framework of valuation-based systems is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.