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Kullback-Leibler Divergence(KLD) Formalism of The Stable Queue with KLD Applications to Biometrics
Version 1
: Received: 17 February 2024 / Approved: 19 February 2024 / Online: 19 February 2024 (14:51:56 CET)
How to cite: A Mageed, D. I. Kullback-Leibler Divergence(KLD) Formalism of The Stable Queue with KLD Applications to Biometrics. Preprints 2024, 2024020979. https://doi.org/10.20944/preprints202402.0979.v1 A Mageed, D. I. Kullback-Leibler Divergence(KLD) Formalism of The Stable Queue with KLD Applications to Biometrics. Preprints 2024, 2024020979. https://doi.org/10.20944/preprints202402.0979.v1
Abstract
The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. More potentially, both service time probability and cumulative functions which make KLDF exact are obtained. The credibility of KLDF is justified through consistency axioms. Some potential applications of Kullback-Leibler divergence to Biometry are presented. The paper concludes with closing remarks combined with some challenging open problems and the next phase of research.
Keywords
Queue; server utilization (SU); short-range interactions; probability density function(PDF); Kullback-Leibler divergence formalism (KLDF); non-extensive maximum extropy(NME); extensive maximum entropy(EME); Biometrics
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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