Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments

Authors:
Thierry Denœux
Affiliations:
HEUDIASYC, UTC, CNRS Centre de Recherche de Royallieu, Compiègne, France F-60205
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

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Abstract

The Transferable BeliefModel (TBM) is a general framework for reasoning with uncertainty using belief functions [8]. Of particular interest is the General Bayesian Theorem (GBT), an extension of Bayes's theorem in which probability measures are replaced by belief functions, and no prior knowledge is assumed [7,6].