The Kullback-Leibler Divergence Class in Decoding the Chest Sound Pattern [PDF]
Informatică economică, 2019 Kullback-Leibler Divergence Class or relative entropy is a special case of broader divergence. It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a
Antonio CLIM, Razvan Daniel ZOTA
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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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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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Kullback–Leibler Divergence Measure for Multivariate Skew-Normal Distributions
Entropy, 2012 The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions.
Reinaldo B Arellano-Valle +1 more
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A New Estimator of Kullback–Leibler Divergence via Shannon Entropy [PDF]
EntropyWe examine the estimation of the Kullback–Leibler (KL) divergence and the use of the goodness-of-fit test for multivariate normality. Our starting point is the maximum entropy principle for Shannon entropy: among all distributions with a fixed mean ...
Mehmet Sıddık Çadırcı +1 more
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Some Order Preserving Inequalities for Cross Entropy and Kullback–Leibler Divergence [PDF]
Entropy, 2018 Cross entropy and Kullback⁻Leibler (K-L) divergence are fundamental quantities of information theory, and they are widely used in many fields. Since cross entropy is the negated logarithm of likelihood, minimizing cross entropy is equivalent to ...
Mateu Sbert +3 more
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Kullback–Leibler Divergence and Mutual Information of Partitions in Product MV Algebras
Entropy, 2017 The purpose of the paper is to introduce, using the known results concerning the entropy in product MV algebras, the concepts of mutual information and Kullback–Leibler divergence for the case of product MV algebras and examine algebraic properties of ...
Dagmar Markechová, Beloslav Riečan
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The AIC Criterion and Symmetrizing the Kullback–Leibler Divergence [PDF]
IEEE Transactions on Neural Networks, 2007 The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as an asymptotically unbiased estimator of a function used for ranking candidate models which is a variant of the Kullback-Leibler divergence between the true model and the approximating candidate model.
Abd-Krim Seghouane, Shun-ichi Amari
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Distributed Vector Quantization Based on Kullback-Leibler Divergence
Entropy, 2015 The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data.
Pengcheng Shen +2 more
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