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
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Coarsening Approximations of Belief Functions
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On transformations of belief functions to probabilities: Research Articles
International Journal of Intelligent Systems - Uncertainty Processing
The canonical decomposition of a weighted belief
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Geometry of Dempster's rule of combination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two New Bayesian Approximations of Belief Functions Based on Convex Geometry
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Three alternative combinatorial formulations of the theory of evidence
Intelligent Data Analysis - Artificial Intelligence
Geometry of relative plausibility and relative belief of singletons
Annals of Mathematics and Artificial Intelligence
On the relative belief transform
International Journal of Approximate Reasoning
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In this paper we prove that a recent Bayesian approximation of belief functions, the relative belief of singletons, meets a number of properties with respect to Dempster's rule of combination which mirrors those satisfied by the relative plausibility of singletons. In particular, its operator commutes with Dempster's sum of plausibility functions, while perfectly representing a plausibility function when combined through Dempster's rule. This suggests a classification of all Bayesian approximations into two families according to the operator they relate to.