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)
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)
Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper documentation of the elicited domain knowledge becomes apparent. To study the usefulness of(More)
Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper documentation of the elicited domain knowledge becomes apparent. To study the usefulness of(More)
The process of engineering probabilistic networks can be supported by a library of generic knowledge structures. Such a knowledge structure is instantiated with domain-specific knowledge and is used to derive, in a number of steps, a segment of the graphical structure of a network. To provide for cus-tomisation to the application at hand, the structures are(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)
This paper describes the interactive reenement of formal speciica-tions of a structured knowledge-based system. The speciication contains an explicit model of the knowledge that is to be used in problem solving and its role in problem solving. This model is used as a bias in the acquisition of domain knowledge. The model acts as bias for reene-ment and(More)