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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)
We present an approach to the solution of decision problems formulated as influence 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 optima] decision policies.
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)
After a brief introduction to causal probabilistic networks and the HUGIN approach, the problem of conflicting data is discussed. A measure of conflict is defined, and it is used in the medical diagnostic system MUNIN. Finally, it is discussed how to distinguish between conflicting data and a rare case.
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)
We have previously reported that, in electron capture dissociation (ECD), rupture of strong intramolecular bonds in weakly bound supramolecular aggregates can proceed without dissociation of weak intermolecular bonds. This is now illustrated on a series of non-specific peptide-peptide dimers as well as specific complexes of modified glycopeptide antibiotics… (More)
Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other… (More)