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Belief revision and information fusion on optimum entropy. (English) Zbl 1101.68944

Summary: This article presents new methods for probabilistic belief revision and information fusion. By making use of the information theoretical principles of optimum entropy (ME principles), we define a generalized revision operator that aims at simulating the human learning of lessons, and we introduce a fusion operator that handles probabilistic information faithfully. This ME-fusion operator satisfies basic demands, such as commutativity and the Pareto principle. A detailed analysis shows it to merge the corresponding epistemic states. Furthermore, it induces a numerical fusion operator that computes the information theoretical mean of probabilities.

MSC:

68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
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