Version 1
: Received: 29 January 2024 / Approved: 30 January 2024 / Online: 31 January 2024 (01:55:50 CET)
How to cite:
A Mageed, D.I. On the Kullback-Leibler Divergence Formalism (Kldf) of the Stable Mg1 Queue Manifold, Its Information Geometric Structure and Its Matrix Exponential. Preprints2024, 2024012092. https://doi.org/10.20944/preprints202401.2092.v1
A Mageed, D.I. On the Kullback-Leibler Divergence Formalism (Kldf) of the Stable Mg1 Queue Manifold, Its Information Geometric Structure and Its Matrix Exponential . Preprints 2024, 2024012092. https://doi.org/10.20944/preprints202401.2092.v1
A Mageed, D.I. On the Kullback-Leibler Divergence Formalism (Kldf) of the Stable Mg1 Queue Manifold, Its Information Geometric Structure and Its Matrix Exponential. Preprints2024, 2024012092. https://doi.org/10.20944/preprints202401.2092.v1
APA Style
A Mageed, D.I. (2024). On the Kullback-Leibler Divergence Formalism (Kldf) of the Stable Mg1 Queue Manifold, Its Information Geometric Structure and Its Matrix Exponential<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202401.2092.v1
Chicago/Turabian Style
A Mageed, D.I. 2024 "On the Kullback-Leibler Divergence Formalism (Kldf) of the Stable Mg1 Queue Manifold, Its Information Geometric Structure and Its Matrix Exponential<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202401.2092.v1
Abstract
The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. It explores the analytic forms of state probabilities and their maximization based on entropy functionals, subject to normalization and mean value constraints. The credibility of KLDF is justified through consistency axioms, and the application of Rényi's and Tsallis's formalisms on a stable M/G/1 queue is examined, resulting in novel state probabilities and insights into information theory of Queue Learning.
Keywords
Queue; server utilisation (SU); short-range interactions; Ricci Curvature Tensor (RCT)
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.
