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The transferable belief model (TBM) is an elaboration on the Dempster–Shafer theory (DST), which is a mathematical model used to evaluate the probability that a given proposition is true from other propositions that are assigned probabilities. It was developed by Philippe Smets who proposed his approach as a response to Zadeh’s example against Dempster's rule of combination. In contrast to the original DST the TBM propagates the open-world assumption that relaxes the assumption that all possible outcomes are known. Under the open world assumption Dempster's rule of combination is adapted such that there is no normalization. The underlying idea is that the probability mass pertaining to the empty set is taken to indicate an unexpected outcome, e.g. the belief in a hypothesis outside the frame of discernment. This adaptation violates the probabilistic character of the original DST and also Bayesian inference. Therefore, the authors substituted notation such as probability masses and probability update with terms such as degrees of belief and transfer giving rise to the name of the method: The transferable belief model.[1][2]
^Ph, Smets (1990). "The combination of evidence in the transferable belief model". IEEE Transactions on Pattern Analysis and Machine Intelligence. 12 (5): 447–458. CiteSeerX 10.1.1.377.5969. doi:10.1109/34.55104.
^Dempster, A.P. (2007). "The Dempster–Shafer calculus for statisticians". International Journal of Approximate Reasoning. 48 (2): 365–377. doi:10.1016/j.ijar.2007.03.004.
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