A Bayesian network can be regarded as a summary of a domain expert’s experience with an implicit population. A database can be regarded as a detailed documentation of such an experience with an explicit population. This connection between Bayesian networks and databases is well recognized and have been pursued for knowledge acquisition [1, 2, 11]. Existing databases are treated as information r...

This paper presents a simple framework for Horn­ clause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and prob­ abilistic notions of evidential reasoning. This can be used as a basis for a new w...

Researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build probabilistic expert systems. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable. We have developed a randomized approximat...

The aim of the article is to show a stochastic approach for both modelling and optimizing the statistical agent belief in a probability model. Two networks are defined: a decision network D of the agent belief state and a utility network U, presenting the utility structure of the agent belief problem. The agent belief is presented via the following three items (B,D,U), where B is a Bayesian net...

In this paper the behavior of various be­ lief network learning algorithms is stud­ ied. Selecting belief networks with cer­ tain minimallity properties turns out to be NP-hard, which justifies the use of search heuristics. Search heuristics based on the Bayesian measure of Cooper and Her­ skovits and a minimum description length (MDL) measure are compared with re­ spect to their properties for...