The study of the interplay between belief and probability has recently been posed in a geometric framework, in which belief and plausibility functions are represented as points of simplices in a Cartesian space. All Bayesian approximations of a belief function b form two homogeneous groups, which we call “affine” and “epistemic” families. In this paper, in particular, we focus on relative plaus...

Naive Bayesian classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this paper we study combining multiple naive Bayesian classifiers by using the hierarchical mixtures of experts system. This novel system, which we call hierarchical mixtures of naive Bayesi...

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...

Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal mod...

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...

Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remai...

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...