Eveline M. Helsper

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Among the tasks involved in building a Bayesian network, obtaining the required probabilities is generally considered the most daunting. Available data collections are often too small to allow for estimating reliable probabilities. Most domain experts, on the other hand, consider assessing the numbers to be quite demanding. Qualitative probabilistic(More)
The study attempted to test the possibility that the center of gravity of two-dimensional patterns is the cue used by a human observer for their localization. Four experiments were carried out. The first, using a matching procedure, required the localization of the center of irregular dot patterns, contour and filled polygons which varied in size and(More)
Building a probabilistic network for a real-life application is a difficult and time-consuming task. Methodologies for building such a network, however, are still lacking. Also, literature on network-specific modelling issues is quite scarce. As we have developed a large proba-bilistic network for a complex medical domain, we have encountered and resolved(More)
Among the various tasks involved in building a Bayesian network for a real-life application, the task of eliciting all probabilities required is generally considered the most daunting. We propose to simplify this task by first acquiring qualitative features of the probability distribution to be represented; these features can subsequently be taken as(More)
The task of eliciting all probabilities required for a Bayesian network can be supported by first acquiring qualitative constraints on the numerical quantities to be obtained. Building upon the concept of qualitative influence, we analyse such constraints and define a small number of influence classes. Based upon these classes, we present a method for(More)
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