A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
A logic-based analysis of Dempster-Shafer theory
International Journal of Approximate Reasoning
The combination of belief: when and how fast?
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Rejoinders to comments on “Perspectives on the theory and practice of belief functions”
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Computational aspects of the Mobius transformation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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A very computationally-efficient Monte-Carlo algorithm for the calculation of Dempster-Shafer belief is described. If Bel is the combination using Dempster's Rule of belief functions Bel1,..., Belm, then, for subset b of the frame Θ, Bel(b) can he calculated in time linear in |Θ| and m (given that the weight of conflict is bounded). The algorithm can also be used to improve the complexity of the Shenoy-Shafer algorithms on Markov trees, and be generalised to calculate Dempster-Shafer Belief over other logics.