Causal probabilistic networks with both discrete and continuous variables

Abstract

An extension of the expert system shell known as handling uncertainty by general influence networks (HUGIN) to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by S.L. Lauritzen, whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete systems. It enables a more natural model of of the domain in question, knowledge acquisition is eased, and the complexity of belief revision is most often reduced considerably. >