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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)
This article describes a propagation scheme for Bayesian networks with conditional Gaussian distributions that does not have the numerical weaknesses of the scheme derived in Lauritzen (1992). The propagation architecture is that of Lauritzen and Spiegelhalter (1988). In addition to the means and variances provided by the previous algorithm , the new(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.
In this paper, we describe the Hugin Tool as an efficient tool for knowledge discovery through construction of Bayesian networks by fusion of data and domain expert knowledge. The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks. The performance of the Hugin Tool is illustrated using real-world(More)
The paper deals with optimality issues in connection with updating beliefs in networks. We address two processes: triangulation and construction of junction trees. In the rst part, we give a simple algorithm for constructing an optimal junction tree from a triangulated network. In the second part, we argue that any exact method based on local calculations(More)