Abstract

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas. 1 Introduction Relationships among symptoms, evidence and diseases are often so complex that they can be described only by very general models. Such models can easily be constructed within the framework of probability theory. Several papers in defense of applying probability theory to AI appeared in the early 1980s (e.g. [2, 18]), and since then probability theory has been widely accepted as a framework for representing and reason...

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