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An expectation operator for belief functions in the Dempster–Shafer theory* * Presented at the 11th Workshop on Uncertainty Processing (WUPES'18), Třeboň, Czech Republic, June 6–9, 2018.View all notes
International Journal of General Systems ( IF 2.259 ) Pub Date : 2019-09-02 , DOI: 10.1080/03081079.2019.1658756
Prakash P. Shenoy

The main contribution of this paper is a new definition of expected value of belief functions in the Dempster–Shafer (D–S) theory of evidence. Our definition shares many of the properties of the expectation operator in probability theory. Also, for Bayesian belief functions, our definition provides the same expected value as the probabilistic expectation operator. A traditional method of computing expected of real-valued functions is to first transform a D–S belief function to a corresponding probability mass function, and then use the expectation operator for probability mass functions. Transforming a belief function to a probability function involves loss of information. Our expectation operator works directly with D–S belief functions. Another definition is using Choquet integration, which assumes belief functions are credal sets, i.e. convex sets of probability mass functions. Credal sets semantics are incompatible with Dempster's combination rule, the center-piece of the D–S theory. In general, our definition provides different expected values than, e.g. if we use probabilistic expectation using the pignistic transform or the plausibility transform of a belief function. Using our definition of expectation, we provide new definitions of variance, covariance, correlation, and other higher moments and describe their properties.
更新日期:2020-03-09

 

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