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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Statistical Estimation of the Kullback–Leibler Divergence [PDF]
Mathematics, 2021 Asymptotic unbiasedness and L2-consistency are established, under mild conditions, for the estimates of the Kullback–Leibler divergence between two probability measures in Rd, absolutely continuous with respect to (w.r.t.) the Lebesgue measure.
Alexander Bulinski, Denis Dimitrov
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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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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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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
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Parameter Estimation Based on Cumulative Kullback–Leibler Divergence
Revstat Statistical Journal, 2021 In this paper, we propose some estimators for the parameters of a statistical model based on Kullback–Leibler divergence of the survival function in continuous setting and apply it to type I censored data.
Yaser Mehrali , Majid Asadi
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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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