Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Dynamic decision making with belief functions
Advances in the Dempster-Shafer theory of evidence
Modeling holistic fuzzy implication using co-copulas
Fuzzy Optimization and Decision Making
The Dempster--Shafer calculus for statisticians
International Journal of Approximate Reasoning
A new combination of evidence based on compromise
Fuzzy Sets and Systems
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Classic Works of the Dempster-Shafer Theory of Belief Functions
Classic Works of the Dempster-Shafer Theory of Belief Functions
Cumulative distribution functions from Dempster-Shafer belief structures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We first introduce the Dempster---Shafer belief structure and highlight its role in the representation of information about a random variable for which our knowledge of the probabilities is interval-valued. We investigate the formation of the cumulative distribution function (CDF) for these types of variables. It is noted that this is also interval-valued and is expressible in terms of plausibility and belief measures. The class of aggregation operators known as copulas are introduced and a number of their properties are provided. We discuss Sklar's theorem, which provides for the use of copulas in the formulation of joint CDFs from the marginal CDFs of classic random variables. We then look to extend these ideas to the case of joining the marginal CDFs associated with Dempster---Shafer belief structures. Finally we look at the formulation CDFs obtained from functions of multiple D---S belief structures.