Results 261 to 270 of about 13,623,354 (322)

Modeling Belief Propensity Degree: Measures of Evenness and Diversity of Belief Functions

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Based on Klir’s framework of uncertainty, the total uncertainty (also called ambiguity) of belief function is linear addition of discord and nonspecificity.
Qianli Zhou, É. Bossé, Yong Deng
semanticscholar   +1 more source

A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion

Information Sciences, 2020
Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty problems in multisensor data fusion. However, the question of how to handle highly conflicting evidence in D–S evidence theory is still an open issue.
Fuyuan Xiao
semanticscholar   +1 more source

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
openaire   +1 more source

Functional belief and judgmental belief

Synthese, 2017
A division between functional (animal) belief, on the one hand, and judgmental (reflective) belief, on the other, is central to Sosa’s two-tier virtue epistemology. For Sosa, mere functional belief is constituted by a first-order affirmation (or, perhaps, a simple disposition to affirm). In contrast, a judgmental belief is an intentional affirmation; a
openaire   +1 more source

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.
openaire   +2 more sources

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, David Harmanec
openaire   +1 more source

Conflict management in information fusion with belief functions

Information Quality in Information Fusion and Decision Making, 2017
In Information fusion, the conflict is an important concept. Indeed, combining several imperfect experts or sources allows conflict. In the theory of belief functions, this notion has been discussed a lot.
Arnaud Martin
semanticscholar   +1 more source

Analyzing belief function networks with conditional beliefs

2011 11th International Conference on Intelligent Systems Design and Applications, 2011
The success of Bayesian networks is due to their capability to simply represent (in)dependence and to be a compact representation of a full joint distribution of the set of random variables involved in the studied system. Since belief function theory is known as a general framework to reason under uncertainty, it is expected that belief function ...
Imen Boukhris, Zied Elouedi, Salem Benferhat   +2 more
openaire   +1 more source

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 \({\mathcal P}^ E\)
openaire   +1 more source

Earth Mover’s divergence of belief function

Computational and Applied Mathematics, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peilin Liu, Fuyuan Xiao
openaire   +2 more sources