Results 1 to 10 of about 58,730 (105)
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
doaj +2 more sources
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
doaj +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
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
doaj +3 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 Megías, Andrés Santos
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
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
doaj +3 more sources