Results 291 to 300 of about 16,108,721 (331)

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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Investigation of activation functions in deep belief network

2017 2nd International Conference on Control and Robotics Engineering (ICCRE), 2017
Deep Belief Network (DBN) is made up of stacked Restricted Boltzmann Machine layers associated with global weight fine-tuning for pattern recognition. However, DBN suffers from vanishing gradient problem due to the saturation characteristic of activation
M. Lau, K. Lim
semanticscholar   +1 more source

A distance for belief functions of orderable set

Pattern Recognition Letters, 2021
Cui-Ping Cheng, Fuyuan Xiao
semanticscholar   +1 more source

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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Basic Belief Functions

Graphical Belief Modeling, 2022
R. Almond
semanticscholar   +1 more source

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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Active learning based on belief functions

Science China Information Sciences, 2020
Shenmin Zhang, Deqiang Han, Yi Yang
semanticscholar   +1 more source