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Proceedings of 6th Workshop on Uncertainty Processing
Vejnarová, Jiřina
The Workshops on Uncertainty Processing have been organized every three years since 1988 and are aimed at fostering creative intellectual activities and exchange of ideas in an informal atmosphere. The Proceedings contain 27 contributions selected by the Programme Committee for presentation at this year's Workshop.
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Measure of divergence of possibility measures
Kroupa, Tomáš
Possibility measures are analyzed from the information-theoretic point of view. It is argued for a significant role of Choquet integration theory in this context. The principal result of the paper is the representation theorem for the nonspecificity of a possibility distribution and the new definition of a~measure of divergence of two possibility measures.
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Noisy-or classifier
Vomlel, Jiří
We discuss application of the noisy-or model to classification with large number of attributes. An example of such a task is categorization of text documents, where attributes are single words from the documents.
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Characterization of inclusion neighbourhood in terms of the essential graph: Lower neighbours
Studený, Milan
The topic of the paper is to characterize inclusion neighbourhood of a given equivalence class of Bayesian networks in terms of the respective essential graph. It is shown that every inclusion neighbour is uniquely described by a pair ([a,b],C) where [a,b] is a pair of distict nodes which is not an edge and C is a disjoint set of nodes. Given such [a,b] the collection of respective sets C is the union of two tufts. The least and maximal sets of these tufts can be read from the essential graph.
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On maximization of the information divergence from an exponential family
Matúš, František ; Ay, N.
The information divergence of a probability measure P from an exponential family E over a finite set is defined as infimum of the divergences of P from Q subject to Q in E. For convex exponential families the local maximizers of this function of P are found. General exponential family E of dimension d is enlarged to an exponential family E* of the dimension at most 3d+2 such that the local maximizers are of zero divergence from E*.
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Transformations of Belief Functions to Probabilities
Daniel, Milan
Alternative approaches to widely known pignistic transformation of belief functios are presented and analyzed. Pignistic, cautions, proportional and disjunctive probabilistic transformations are examined from the point of view of their interpretation, of decision making and from the point of view of their communication with rules (operators) for belief function combination.
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Possibilistic Laws of Large Numbers
Kramosil, Ivan
We consider sequences of samples defined on spaces endowed by a possibilistic measure, looking for relatively small sets of such sequences which are important or interesting, in a sense, and which occur with possibility degree equal to one or tending to one with the length of the sample sequence increasing.
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