We address the problem of causal interpre­ tation of the graphical structure of Bayesian belief networks (BBNs). We review the con­ cept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise ·, when mechanisms are placed in the conte...

Bayesian Belief Networks (BBNs) are a pow­ erful formalism for reasoning under uncer­ tainty but bear some severe limitations: they require a large amount of information be­ fore any reasoning process can start, they have limited contradiction handling capabil­ ities, and their ability to provide explana­ tions for their conclusion is still controversial. There exists a class of reasoning syste...

Concepts like marginal probability, changes in probability, probability propagation and cause-effect relationships are important when reasoning about causality and uncertainty. To help people to understand these concepts, a Bayes net visualization tool (VisNet) has been developed. VisNet shows how temporal order, colour, size, proximity (closeness), and animation techniques can be used to visua...

clean groundwater (<0.1 μg/l) for theHavelse catchment area in the future (30-50 years).This overall Boolean indicator variable provides anoverall estimate based on content of pesticides ingroundwater, shallow groundwater and surface water.The first is weighted highest for the total “score”. Therisk of pollution from surface water can eventually beminimised significa...

Belief Networks in the Bayesian approach provide a wellestablished methodology to fuse prior knowledge and statistical observations for an enriched decision support. In this paper we investigate one of the advantages of the Bayesian approach the provided additional uncertainty information for predictions in a medical classification problem. We perform a Bayesian analysis using Belief Network mo...

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in sy...

105 WORDS) This paper introduces the use of Bayesian Belief Networks for the analysis of survey data to investigate the relationship between land use and transportation. Bayesian statistics are used to reason under uncertainty and provide the basis for a methodological approach that does not require stringent a priori assumptions about the statistical model used to analyze the data. This study ...