Building blocks for graphical belief models

The graphical belief model is a versatile tool for modeling complex systems. The graphical structure and its implicit probabilistic and logical independence conditions define the relationships between many of the variables of the problem. The graphical model is composed of a collection of local models:models of both interactions between the variables sharing a common hyperedge and information about single variables. These local models can be constructed with either probability distributions or belief functions. This paper takes the latter approach and describes simple models for univariate and multivariate belief functions. The examples are taken from both reliability and knowledge representation problems.