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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)
In this paper we present a junction tree based inference architecture exploiting the structure of the original Bayesian network and independence relations induced by evidence to improve the efficiency of inference. The efficiency improvements are obtained by maintaining a multiplicative decomposition of clique and separator potentials. Maintaining a(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)
As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient in­ ference methods is an ongoing effort. Mul­ tiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for flexible modeling and distributed inference. Lazy propagation(More)
We present an approach to efficiently generating an inspection strategy for fault diagnosis. We extend the traditional troubleshooting framework to model non-perfect repair actions, and we include questions. Questions are troubleshooting steps that do not aim at repairing the device, but merely are performed to capture information about the failed(More)
We present an approach to the solution of decision problems formulated as innuence diagrams. This approach involves a special tri-angulation of the underlying graph, the construction of a junction tree with special properties , and a message passing algorithm operating on the junction tree for computation of expected utilities and optimal decision policies.