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Conditional Belief Functions Versus Proper Belief Functions
2003Dempster-Shafer conditional belief functions are generally not usable because composition of conditional belief functions is not granted to yield joint multivariate belief distribution, as some values of the belief distribution may be negative [1,3].
Mieczyslaw Alojzy Klopotek +1 more
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Bayesian updating and belief functions
IEEE Transactions on Systems, Man, and Cybernetics, 1992In a situation of uncertainty, the probability measure \(P\) is only known to belong to some set of probability measures \(\mathcal P\). Having observed a certain event \(E\), the true probability measure belongs to the set \({\mathcal P}^ E\) of conditionals of the members \(P\) of \(\mathcal P\) with respect to \(E\). Representing \({\mathcal P}^ E\)
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Beliefs as signals: A new function for belief
Philosophical Psychology, 2017AbstractBeliefs serve at least two broad functions. First, they help us navigate the world. Second, they serve as signals to manipulate others. Philosophers and psychologists have focused on the first function while largely overlooking the second. This article advances a conception of signals and makes a prima facie case for a social signaling function
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Contextual Discounting of Belief Functions
2005The Transferable Belief Model is a general framework for managing imprecise and uncertain information using belief functions. In this framework, the discounting operation allows to combine information provided by a source (in the form of a belief function) with metaknowledge regarding the reliability of that source, to compute a “weakened”, less ...
David Mercier +2 more
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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 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 Decombination of Belief Function
2019 22th International Conference on Information Fusion (FUSION), 2019Deqiang Han, Yi Yang 0008, Jean Dezert
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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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Belief functions with nonstandard values
1997The notions of basic probability assignment and belief function, playing the basic role in the Dempster-Shafer model of uncertainty quantification and processing often called Dempster-Shafer theory, are generalized in such a way that their values are not numbers from the unit interval of reals, but rather infinite sequences of real numbers including ...
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