Results 1 to 10 of about 59,513 (222)
Computation of Kullback–Leibler Divergence in Bayesian Networks [PDF]
Entropy, 2021 Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q.
Serafín Moral +2 more
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Entropy, 2016 Information-theoretic measures, such as the entropy, the cross-entropy and the Kullback–Leibler divergence between two mixture models, are core primitives in many signal processing tasks.
Frank Nielsen, Ke Sun
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Kullback–Leibler Divergence of an Open-Queuing Network of a Cell-Signal-Transduction Cascade [PDF]
Entropy, 2023 Queuing networks (QNs) are essential models in operations research, with applications in cloud computing and healthcare systems. However, few studies have analyzed the cell’s biological signal transduction using QN theory.
Tatsuaki Tsuruyama
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Exact Expressions for Kullback–Leibler Divergence for Multivariate and Matrix-Variate Distributions [PDF]
EntropyThe Kullback–Leibler divergence is a measure of the divergence between two probability distributions, often used in statistics and information theory. However, exact expressions for it are not known for multivariate or matrix-variate distributions apart ...
Victor Nawa, Saralees Nadarajah
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On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
Entropy, 2019 The Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution.
Frank Nielsen
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Kullback–Leibler Divergence of a Freely Cooling Granular Gas [PDF]
Entropy, 2020 Finding the proper entropy-like Lyapunov functional associated with the inelastic Boltzmann equation for an isolated freely cooling granular gas is a still unsolved challenge.
Alberto Megías, Andrés Santos
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A data assimilation framework that uses the Kullback-Leibler divergence. [PDF]
PLoS ONE, 2021 The process of integrating observations into a numerical model of an evolving dynamical system, known as data assimilation, has become an essential tool in computational science.
Sam Pimentel, Youssef Qranfal
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Kullback-Leibler Divergence and Mutual Information of Experiments in the Fuzzy Case
Axioms, 2017 The main aim of this contribution is to define the notions of Kullback-Leibler divergence and conditional mutual information in fuzzy probability spaces and to derive the basic properties of the suggested measures.
Dagmar Markechová
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Backward cloud transformation algorithm based on Kullback Leibler divergence [PDF]
PLoS ONEAs a bidirectional cognitive model for dealing with uncertainty, cloud model (CM) are commonly used in application scenarios such as fault diagnosis, system modeling, and evaluation.
Xiaobin Xu +6 more
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Exact Expressions for Kullback–Leibler Divergence for Univariate Distributions [PDF]
EntropyThe Kullback–Leibler divergence (KL divergence) is a statistical measure that quantifies the difference between two probability distributions. Specifically, it assesses the amount of information that is lost when one distribution is used to approximate ...
Victor Nawa, Saralees Nadarajah
doaj +2 more sources