Aggregating Imprecise Probabilistic Knowledge: application to Zadeh's paradox and sensor networks

Authors: Alessio Benavoli and Alessandro Antonucci

Abstract: The problem of aggregating two or more sources of information containing knowl- edge about a common domain is considered. We propose an aggregation frame- work for the case where the available information is modelled by coherent lower previsions, corresponding to convex sets of probability mass functions. The con- sistency between aggregated beliefs and sources of information is discussed. A closed formula, which specializes our rule to a particular class of models, is also derived. Two applications consisting in a possible explanation of Zadeh’s paradox and an algorithm for estimation fusion in sensor networks are finally reported.

Year: 2010.

Details: International Journal of Approximate Reasoning 51(9), 1014-1028.

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