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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)
A scheme is presented for modelling and local computation of exact probabilities, means and variances for mixed qualitative and quantitative variables. The models assume that the conditional distribution of the quantitative variables, given the qualitative, is multivariate Gaussian. The computational architecture is set up by forming a tree of belief(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)
A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects. The parameters of the model were based on experimental data from the literature describing insulin and carbohydrate(More)
An extension of the expert system shell HUGIN to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by Lauritzen (1992), whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete(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)