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].
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Denœux, T. (2007). Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_1
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