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ISSN: 1549-3644 (Print)
ISSN: 1558-6359 (Online)
Research Article Open Access

Lossy Asymptotic Equipartition Property for Networked Data Structures

Kwabena Doku-Amponsah1
  • 1 University of Ghana, Ghana

Abstract

In this study, we prove a Generalized Information Theory for Networked Data Structures modelled as random graphs. The main tools in this study remain large deviation principles for properly defined empirical measures on random graphs. To motivate the paper, we apply our main result to a concrete example from the field of Biology.

Journal of Mathematics and Statistics
Volume 13 No. 2, 2017, 152-158

DOI: https://doi.org/10.3844/jmssp.2017.152.158

Submitted On: 18 September 2016 Published On: 24 May 2017

How to Cite: Doku-Amponsah, K. (2017). Lossy Asymptotic Equipartition Property for Networked Data Structures. Journal of Mathematics and Statistics, 13(2), 152-158. https://doi.org/10.3844/jmssp.2017.152.158

Open AccessCopyright: © 2017 Kwabena Doku-Amponsah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Keywords

  • Asymptotic Equipartition Property
  • Rate-Distortion Theory
  • Process-Level Large Deviation Principle
  • Relative Entropy
  • Random Network
  • Metabolic Network