Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty, belief, and probability
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Interval structure: a framework for representing uncertain information
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Belief and surprise: a belief-function formulation
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Evidential reasoning in a categorial perspective: conjunction and disjunction of belief functions
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Signaling game-based approach to power control management in wireless networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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Belief functions are mathematical objects defined to satisfy three axioms that look somewhat similar to the axioms defining probability functions. We argue that there are (at least) two useful and quite different ways of understanding belief functions. The first is as a generalized probability function (which technically corresponds to the lower envelope or infimum of a family of probability functions). The second is as a way of representing evidence. Evidence, in turn, can be understood as a mapping from probability functions to probability functions. It makes sense to think of updating a belief if we think of it as a generalized probability. On the other hand, it makes sense to combine two beliefs (using, say, Dempster's rule of combination) only if we think of the belief functions as representing evidence. Many previous papers have pointed out problems with the belief function approach; the claim of this paper is that these problems can be explained as a consequence of confounding these two views of belief functions.