TE1: Bayesian networks with imprecise probabilities: theory and applications to knowledge-based systems and classification
Tutorialists: Alessandro Antonucci, Giorgio Corani, Denis Mauà
Monday, August 5th, morning
"Bayesian networks are important tools for uncertain reasoning in AI; their quantification requires a precise assessment of the conditional probabilities. Credal networks generalize Bayesian networks, so that probabilities can vary in a set (e.g., interval). This provides a more realistic model of expert knowledge and returns more robust inferences. The first part of this tutorial describes the specification procedure for credal network, the existing inference algorithms and approaches to decision making; two prototypical examples of knowledge-based expert systems related to military decision making and environmental risk analysis based on credal networks are indeed presented. In the second part, we describe the major examples of credal classifiers, i.e., classification algorithms based on credal networks, developed so far. Credal classifiers generalize the traditional Bayesian classifiers, which are based on a single prior density and on a single likelihood. Credal classifiers are instead based on (i) a set of priors, thus removing the need for subjectively choosing a prior and (ii) possibly also on a set of likelihoods, to allow robust classification even with missing data. Credal classifiers can return more classes if the assignment to a single class is too uncertain; in this way, they preserve reliability. The tutorial presents algorithms for credal classification and comparison with traditional classifiers on a large number of data sets. Also the problem of evaluating performance of a classifier possibly returning multiple output and alternative quantification techniques are discussed."