Concentration of Measure Inequalities in Information Theory, Communications, and Coding: Second Edition

Maxim Raginsky, Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, United States, maxim@illinois.edu Igal Sason, Department of Electrical Engineering, Technion – Israel Institute of Technology, Israel, sason@ee.technion.ac.il
Published: 17 Sep 2014
© 2014 M. Raginsky and I. Sason
 
Subjects
Coding theory and practice,  Information theory and statistics,  Multiuser information theory,  Shannon theory
 
ISBN: 978-1-60198-906-2
260 pp. $99.00
Buy book
 
ISBN: 978-1-60198-907-9
260 pp. $240.00
Buy E-book
Table of contents:
1. Introduction
2. Concentration Inequalities via the Martingale Approach
3. The Entropy Method, Logarithmic Sobolev Inequalities, and Transportation-Cost Inequalities
Acknowledgments
References

Concentration of Measure Inequalities in Information Theory, Communications, and Coding

This second edition includes several new sections and provides a full update on all sections. This book was welcomed when it was first published as an important comprehensive treatment of the subject which is now brought fully up to date.

Concentration inequalities have been the subject of exciting developments during the last two decades, and have been intensively studied and used as a powerful tool in various areas. These include convex geometry, functional analysis, statistical physics, mathematical statistics, pure and applied probability theory (e.g., concentration of measure phenomena in random graphs, random matrices, and percolation), information theory, theoretical computer science, learning theory, and dynamical systems.

Concentration of Measure Inequalities in Information Theory, Communications, and Coding focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding. In addition to being a survey, this monograph also includes various new recent results derived by the authors.

Concentration of Measure Inequalities in Information Theory, Communications, and Coding is essential reading for all researchers and scientists in information theory and coding.

 
9781601989062

Concentration of Measure Inequalities in Information Theory, Communications, and Coding Update | 0100000064_Erratum.pdf

This is an erratum to the update to M. Raginsky and I. Sason, "Concentration of Measure Inequalities in Information Theory, Communications, and Coding," Foundations and Trends® in Communications and Information Theory, vol. 10, no. 1–2, pp. 1-246, Oct. 2013. doi: 10.1561/0100000064.

Companion

Concentration of Measure Inequalities in Information Theory, Communications, and Coding , CIT, Volume 10, Issue 1-2 10.1561/0100000064
This is the first edition of Concentration of Measure Inequalities in Information Theory, Communications, and Coding