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Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell-for handling a domain model expressed by a causal(More)
The paper describes aHUGIN, a tool for cre­ ating adaptive systems. aHUGIN is an exten­ sion of the HUG IN shell, and is based on the methods reported by Spiegelhalter and Lau­ ritzen {1990a). The adaptive systems result­ ing from aHUGIN are able to adj ust the con­ ditional probabilities in the modeL A short analysis of the adaptation task is given and the(More)
The authors present a method for decomposition of Bayesian networks into their maximal prime subgraphs. The correctness of the method is proven and results relating the maximal prime subgraph decomposition (MPD) to the maximal complete subgraphs of the moral graph of the original Bayesian network are presented. The maximal prime subgraphs of a Bayesian(More)
As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilis-tic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as(More)
Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between nodes. Various types of models are described(More)
This paper describes the diagnostic function of a prototype expert system for electromyography (EMG). The prototype was restricted to a limited "Microhuman" anatomy with only 6 muscles and 8 nerves, and a corresponding limitation on the number of local nerve lesions. It attempted to give a detailed description of the most important groups of generalized(More)
window in figure 1. Most methods for exact probability propagation in Bayesian networks do not carry out the inference directly over the network, but over a secondary structure known as a junction tree or a join tree (JT). The process of obtaining a J T is usually termed compilation. As compilation is usually viewed as a whole process; each time the network(More)