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Coarsening Approximations of Belief Functions
2001A method is proposed for reducing the size of a frame of discernment, in such a way that the loss of information content in a set of belief functions is minimized. This approach allows to compute strong inner and outer approximations which can be combined efficiently using the Fast Mobius Transform algorithm.
Amel Ben Yaghlane +2 more
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Belief functions versus probability functions
1988Two models are proposed to quantify someone's degree of belief, based respectively on probability functions, the Bayesian model, and on belief functions, the transferable belief model (Shafer 1976). The first, and by far the oldest, is well established and supported by excellent axiomatic and behaviour arguments.
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On Decombination of Belief Function
2019 22th International Conference on Information Fusion (FUSION), 2019Deqiang Han, Yi Yang 0008, Jean Dezert
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On transformations of belief functions to probabilities
International Journal of Intelligent Systems, 2006Summary: Alternative approaches to the widely known pignistic transformation of belief functions are presented and analyzed. Pignistic, cautious, proportional, and disjunctive probabilistic transformations are examined from the point of view of their interpretation, of decision making and (from the point of view) of their commutation with rules ...
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On Belief Functions and Random Sets
2012We look back at how axiomatic belief functions were viewed as distributions of random sets, and address the problem of joint belief functions in terms of copulas. We outline the axiomatic development of belief functions in the setting of incidence algebras, and some aspects of decision-making with belief functions.
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Probability of deductibility and belief functions
2005We present an interpretation of Dempster-Shafer theory based on the probability of deducibility. We present two forms of revision (conditioning) that lead to the geometrical rule of conditioning and to Dempster rule of conditioning, respectively.
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The application of the matrix calculus to belief functions
International Journal of Approximate Reasoning, 2002Philippe Smets
exaly
A new distance-based total uncertainty measure in the theory of belief functions
Knowledge-Based Systems, 2016Deqiang Han
exaly