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An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics

  • Alessandro Antonucci
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Evidence theory extends Bayesian probability theory by allowing for a more expressive model of subjective uncertainty. Besides standard interpretation of belief functions, where uncertainty corresponds to probability masses which might refer to whole subsets of the possibility space, credal semantics can be also considered. Accordingly, a belief function can be identified with the whole set of probability mass functions consistent with the beliefs induced by the masses. Following this interpretation, a novel, set-valued, dissimilarity measure with a clear behavioral interpretation can be defined. We describe the main features of this new measure and comment the relation with other measures proposed in the literature.

Keywords

Mass Function Dissimilarity Measure Probability Mass Function Belief Function Manhattan Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.IDSIA, Istituto Dalle Molle di Studi sull’Intelligenza ArtificialeManno-LuganoSwitzerland

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