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.
Andres Cano +2 more
exaly +8 more sources
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.
Denis Dimitrov
exaly +5 more sources
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 Megias +2 more
exaly +4 more sources
Rényi Divergence and Kullback-Leibler Divergence [PDF]
IEEE Transactions on Information Theory, 2014 Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon's entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends on a parameter that is called its order.
Peter Harremoës
exaly +4 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +3 more sources
A decision cognizant Kullback–Leibler divergence
Pattern Recognition, 2017 In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback–Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback–Leibler divergence
Teófilo De Campos +2 more
exaly +6 more sources
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