A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Belief functions versus probability functions
Proceedings of the 2nd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems on Uncertainty and intelligent systems
A computationally efficient approximation of Dempster-Shafer theory
International Journal of Man-Machine Studies
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Consonant approximation of belief functions
International Journal of Approximate Reasoning
A Monte-Carlo algorithm for Dempster-Shafer belief
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Monte-Carlo methods make Dempster-Shafer formalism feasible
Advances in the Dempster-Shafer theory of evidence
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Particle Filtering with Evidential Reasoning
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Credal semantics of Bayesian transformations in terms of probability intervals
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Faithful approximations of belief functions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved, Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations. It presents the empirical findings and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods.