Fusion, propagation, and structuring in belief networks
Artificial Intelligence
The principle of minimum specificity as a basis for evidential reasoning
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IEEE Transactions on Systems, Man and Cybernetics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Axioms for probability and belief-function propagation
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International Journal of Approximate Reasoning
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Hierarchical evidence and belief functions
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
International Journal of Approximate Reasoning
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Intelligent Data Analysis - Artificial Intelligence
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International Journal of Information Technology and Management
Geometry of relative plausibility and relative belief of singletons
Annals of Mathematics and Artificial Intelligence
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International Journal of Approximate Reasoning
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UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
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International Journal of Approximate Reasoning
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International Journal of Approximate Reasoning
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Engineering Applications of Artificial Intelligence
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The goal of this paper is to compare the similarities and differences between Bayesian and belief function reasoning. Our main conclusion is that although there are obvious differences in semantics, representations, and the rules for combining and marginalizing representations, there are many similarities. We claim that the two calculi have roughly the same expressive power. Each calculus has its own semantics that allow us to construct models suited for these semantics. Once we have a model in either calculus, one can transform it to the other by means of a suitable transformation.