A computationally efficient approximation of Dempster-Shafer theory
International Journal of Man-Machine Studies
Consonant approximation of belief functions
International Journal of Approximate Reasoning
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Monte-Carlo methods make Dempster-Shafer formalism feasible
Advances in the Dempster-Shafer theory of evidence
Decision analysis using belief functions
Advances in the Dempster-Shafer theory of evidence
On decision making using belief functions
Advances in the Dempster-Shafer theory of evidence
Dynamic decision making with belief functions
Advances in the Dempster-Shafer theory of evidence
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Approximations for decision making in the Dempster-Shafer theory of evidence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Coarsening Approximations of Belief Functions
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
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A conceptual foundation for approximation of belief functions is proposed and investigated. It is based on the requirements of consistency and closeness. An optimal approximation is studied. Unfortunately, the computation of the optimal approximation turns out to be intractable. Hence, various heuristic methods are proposed and experimantally evaluated both in terms of their accuracy and in terms of the speed of computation. These methods are compared to the earlier proposed approximations of belief functions.