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Algorithms for dempstershafer theory
 Algorithms for Uncertainty and Defeasible Reasoning
, 2000
"... The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90] ..."
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Cited by 20 (3 self)
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The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90
The DempsterShafer calculus for statisticians
 International Journal of Approximate Reasoning
, 2007
"... The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “ ..."
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Cited by 46 (1 self)
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The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability
Combination of evidence in DempsterShafer theory
, 2002
"... DempsterShafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. DempsterShafer theory does not require a ..."
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Cited by 79 (2 self)
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expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for DempsterShafer structures and provides examples of the implementation
DEMPSTERSHAFER INFERENCE WITH WEAK BELIEFS
"... Beliefs specified for predicting an unobserved realization of pivotal variables in the context of the fiducial and DempsterShafer (DS) inference can be weakened for credible inference. We consider predictive random sets for predicting an unobserved random sample from a known distribution, e.g., t ..."
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Cited by 15 (11 self)
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Beliefs specified for predicting an unobserved realization of pivotal variables in the context of the fiducial and DempsterShafer (DS) inference can be weakened for credible inference. We consider predictive random sets for predicting an unobserved random sample from a known distribution, e
DempsterShafer theory of evidence
, 1994
"... comprehensive comparison between generalized incidence calculus and the ..."
DempsterShafer for Anomaly Detection
"... Abstract—In this paper, we implement an anomaly detection system using the DempsterShafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach t ..."
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to a realworld email dataset the algorithm works for email worm detection. DempsterShafer can be a promising method for anomaly detection problems with multiple features (data sources), and two or more classes. I.
1 Classic Works of the DempsterShafer Theory of Belief Functions: An Introduction ∗
"... Abstract. In this chapter, we review the basic concepts of the theory of belief functions and sketch a brief history of its conceptual development. We then provide an overview of the classic works and examine how they established a body of ..."
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Abstract. In this chapter, we review the basic concepts of the theory of belief functions and sketch a brief history of its conceptual development. We then provide an overview of the classic works and examine how they established a body of
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 482 (8 self)
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with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. tree
Results 1  10
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606,146