No abstract

No abstract

Causal probabilistic networks (CPNs) have proved to be a useful knowledge representation tool for modeling domains where causal relationsin a broad sense -are a natural way of relating domain concepts and where uncertainty is inherited in these relations. The domain is modeled in a CPN by use of a directed graph where the nodes represent concepts in the domain and the arcs represent causal relations. Furthermore, the quantitative relation between a node and its immediate causes is expressed as conditional probabilities. During the last few years, several schemes based on probability theory for incorporating and propagating new information throughout a CPN has emerged. As long as the domain can be modeled by use of a singly connected CPN (i.e., no more than one path between any pair of nodes), the schemes operate directly in the CPN and perform conceptually simple operations in this structure. When it comes to more complicated structures such as multiply connected CPNs (i.e., more than one path is allowed between pairs of nodes), the schemes operate in derived structures where the embedded domain knowledge no longer is as explicit and transparent as in the CPN. Furthermore, the simplicity in the operations is lost also. This report outlines a scheme-the algebra of Bayesian belief universesfor absorbing and propagating evidence in multiply connected CPNs. The scheme provides a secondary structure, a junction tree, and a simple set of algebraic operations between objects in this structure, CollectEvidence and DistributeEvidence. These are the basic tools for making inference in a CPN domain model and yield a calculus as simple as in the case of singly connected CPNs.*This work is supported in part by the EEC ESPRIT programme, project P599.

No abstract

We present an approach to the solution of de cision problems formulated as influence dia grams. This approach involves a special tri angulation of the underlying graph, the con struction of a junction tree with special prop erties, and a message passing algorithm op erating on the junction tree for computa tion of expected utilities and optimal decision policies.

No abstract

No abstract

Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications, and point to ongoing research that is relevant for practitioners in reliability.