The most widely used updating rule for non-additive probabilities is the Dempster-Schafer rule. Schmeidler and Gilboa have developed a model of decision making under uncertainty based on non-additive probabilities, and in their paper "Updating Ambiguous Beliefs" they justify the Dempster-Schafer rule based on a maximum likelihood procedure. This note shows in the context of Schmeidler-Gilboa pr...

The Dempster-Shafer “theory of evidence” encompasses and extends the Bayes Theorem-based decision making machinery. Dempster-Shafer’s innovation is the introduction of lower and upper bounds, designated “belief” and “plausibility”, that are attached to probability estimates. The Dempster-Shafer algebra provides for propagation of and reasoning about these quantities according to an algebra whos...

The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. The Dempster rule of evidence combination corresponds to the join operator of the relational database theory. This rough-set based interpretation is qualitative in nature and can represent a number of belief function operators.

A simple Monte-Carlo algorithm can be used to calculate Dempster-Shafer belief very efficiently unless the conflict between the evidences is very high. This paper introduces and explores Markov Chain Monte-Carlo algorithms for calculating Dempster-Shafer belief that can also work well when the conflict is high.

We formulate the Dempster-Shafer formalism of belief functions [Shafer 761 in the spirit of logical inference systems. Our formulation (called the belief calculus) explicitly avoids the use of set-theoretic notations. As such, it serves as an alternative for the use of the Dempster-Shafer formalism for uncertain reasoning.