Importance Sampling Monte-Carlo Algorithms for the Calculation of Dempster-Shafer Belief

This paper presents importance sampling Monte-Carlo algorithms for the calculation of belief functions combination. When the con-ict between the evidence is not very high a simple Monte-Carlo algorithm can produce good quality estimations. For the case of highly connicting evidences a Markov chain Monte-Carlo algorithm was also proposed. In this paper, a new class of importance sampling based algorithms is presented. The performance of them is compared by experimental tests.

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