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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
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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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
Approximations for Decision Making in the DempsterShafer Theory of Evidence
 In Uncertainty in Artificial Intelligence
, 1996
"... The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief ..."
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Cited by 12 (0 self)
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The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief
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
Calculating DempsterShafer Plausibility
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1991
"... A sucient condition is developed for the equality of the plausibility and commonality measures of the DempsterShafer belief calculus. When the condition is met, as it is in many applications, an ecient method to calculate relative plausibility is available. In particular, the method can be used ..."
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Cited by 12 (1 self)
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. 1 Summary The DempsterShafer belief calculus [7] is a theory of plausible reasoning because it provides methods to represent and combine weights of evidence. The measures Bel and Pl are used to assist decision making and play roles similar to that played by distribution functions in probability
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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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
Perceptual Organization Approach Based On DempsterShafer Theory
"... In this paper, we propose an application of the perceptual organization based on the DempsterShafer theory. This method is divided into two parts which respectively rectifies the segmentation mistakes by restoring the coherence of the segments and detects objects in the scene by forming groups of ..."
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Cited by 8 (1 self)
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In this paper, we propose an application of the perceptual organization based on the DempsterShafer theory. This method is divided into two parts which respectively rectifies the segmentation mistakes by restoring the coherence of the segments and detects objects in the scene by forming groups
Approximation Algorithms and Decision Making in the DempsterShafer Theory of Evidence  An Empirical Study
 International Journal of Approximate Reasoning
, 1996
"... The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the major points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief ..."
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Cited by 59 (0 self)
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The computational complexity of reasoning within the DempsterShafer theory of evidence is one of the major points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements
DempsterShafer theory and statistical inference with weak beliefs
, 2008
"... DempsterShafer (DS) theory is a powerful tool for probabilistic reasoning and decisionmaking based on a formal calculus for combining statistical and nonstatistical evidence, as represented by a system of belief functions. DS theory has been widely used in computer science and engineering applica ..."
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Cited by 21 (16 self)
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DempsterShafer (DS) theory is a powerful tool for probabilistic reasoning and decisionmaking based on a formal calculus for combining statistical and nonstatistical evidence, as represented by a system of belief functions. DS theory has been widely used in computer science and engineering
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