Results 251 to 260 of about 5,670,665 (286)

APPROXIMATION OF BELIEF FUNCTIONS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003
This paper addresses the approximation of belief functions by probability functions where the approximation is based on minimizing the Euclidean distance. First of all, we simplify this optimization problem so it becomes equivalent to a standard problem in linear algebra.
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ON THE POLYTOPES OF BELIEF AND PLAUSIBILITY FUNCTIONS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2010
In this paper we study some properties of the polytope of belief functions on a finite referential. These properties can be used in the problem of identification of a belief function from sample data. More concretely, we study the set of isometries, the set of invariant measures and the adjacency structure.
Pedro Miranda 0002, Elías F. Combarro
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On Belief Functions.

1992
The present state of development of Dempster-Shafer theory is surveyed and its place among theories of dealing with uncertainty in AI is discussed. No knowledge of the theory is assumed.
Petr Hájek 0001, David Harmanec
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Bayesian updating and belief functions

IEEE Transactions on Systems, Man, and Cybernetics, 1992
In 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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Conditional Belief Functions Versus Proper Belief Functions

2003
Dempster-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, Slawomir Tadeusz Wierzchon   +1 more
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Beliefs as signals: A new function for belief

Philosophical Psychology, 2017
AbstractBeliefs 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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Implementing belief function computations

International Journal of Intelligent Systems, 2003
Summary: This article discusses several implementation aspects for Dempster-Shafer belief functions. The main objective is to propose an appropriate representation of mass functions and efficient data structures and algorithms for the two basic operations of combination and marginalization.
Rolf Haenni, Norbert Lehmann
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Coarsening Approximations of Belief Functions

2001
A 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, Thierry Denoeux, Khaled Mellouli   +2 more
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Belief functions versus probability functions

1988
Two 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), 2019
Deqiang Han, Yi Yang 0008, Jean Dezert
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