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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.

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)

We extend the language of influence diagrams to cope with decision scenarios where the order of decisions and observations is not determined. As the ordering of decisions is dependent on the evidence, a step-strategy of such a scenario is a sequence of dependent choices of the next action. A strategy is a step-strategy together with selection functions for… (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.

The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical… (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)