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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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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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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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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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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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
Kullback Leibler divergence in complete bacterial and phage genomes [PDF]
PeerJ, 2017 The amino acid content of the proteins encoded by a genome may predict the coding potential of that genome and may reflect lifestyle restrictions of the organism.
Sajia Akhter +5 more
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Local inconsistency detection using the Kullback–Leibler divergence measure [PDF]
Systematic ReviewsBackground The standard approach to local inconsistency assessment typically relies on testing the conflict between the direct and indirect evidence in selected treatment comparisons.
Loukia M. Spineli
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Divergence Measure of Belief Function and Its Application in Data Fusion
IEEE Access, 2019 Divergence measure is widely used in many applications. To efficiently deal with uncertainty in real applications, basic probability assignment (BPA) in Dempster-Shafer evidence theory, instead of probability distribution, is adopted.
Yong Deng
exaly +3 more sources