ion in Bayesian Belief Networks and Automatic Discovery From Past Inference Sessions * Wai Lam Department of Computer Science University of Waterloo Waterloo, Ontario, Canada, N2L 3Gl wlamlQlogos.uwaterloo.ca

Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets' {Smets:93} directed ac...

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence es...

We describe how to combine probabilistic logic and Bayesian networks to obtain a new framework (\Bayesian logic") for dealing with uncertainty and causal relationships in an expert system. Probabilistic logic, invented by Boole, is a technique for drawing inferences from uncertain propositions for which there are no independence assumptions. A Bayesian network is a \belief net" that can represe...

The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offer a possibility to analyse nominal data in a more sophisticated way. The possibility to indicate a structure via graphical representation, where variables are nodes and relationships are edges, enriches this method and makes it a powerful tool for data analysis. In this paper, an overview on Baye...

In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tractable, in the sense that the posterior is also decomposable and can be completely determined ana­ lytically in polynomial time. This fol­ lows from two main results: First, we show that factored distributions over spanni...

The maintenance of equipment, machinery and facilities is a vital part of the industrial process and requires millions of man-hours of technician time. A significant portion of this time is devoted to troubleshooingt system malfirnctions. We develop an automated system that uses Bayesian Belief Networks (BBNs) for effective troubleshooting. BBNs are ideal paradigms to represent the causality an...

Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable conngurations given an observed evidence. These conngurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we study how to simplify the explanations in such a way that the resulting con...