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Propagation of Gaussian belief functions

1996
A Gaussian belief function (GBF) can be intuitively described as a Gaussian distribution over a hyperplane, whose parallel sub-hyperplanes are the focal elements. It includes as special cases non-probabilistic linear equations, statistical observations, multivariate Gaussian distributions, and vacuous belief functions.
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Belief functions with nonstandard values

1997
The 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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Probability of deductibility and belief functions

2005
We 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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Application of belief functions to medical image segmentation: A review

Information Fusion, 2023
Su Ruan, Thierry Denoeux, Ling Huang
exaly  

The Internal Conflict of a Belief Function

2012
In this paper we define and derive an internal conflict of a belief function We decompose the belief function in question into a set of generalized simple support functions (GSSFs). Removing the single GSSF supporting the empty set we obtain the base of the belief function as the remaining GSSFs.
openaire   +2 more sources

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

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Yong Deng, Éloi Bossé, Qianli Zhou
exaly  

Analyzing the combination of conflicting belief functions

Information Fusion, 2007
Philippe Smets
exaly  

Refined modeling of sensor reliability in the belief function framework using contextual discounting

Information Fusion, 2008
Benjamin Quost   +2 more
exaly  

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