By 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

Publication Date: 18 Dec 2018

Suggested Citation: Maxim Raginsky, Igal Sason (2018), "Concentration of Measure Inequalities in Information Theory, Communications, and Coding: Third Edition", Boston-Delft: now publishers, http://dx.doi.org/10.1561/9781680835359

Downloaded: 2 times

Description

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, 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.

This third edition of the bestselling book introduces the reader to the martingale method and the Efron-Stein-Steele inequalities in completely new sections. A converse of the Herbst argument, and an extended discussion on the HWI inequalities have been incorporated into the chapter on the entropy method and transportation-cost inequalities. A new application of the entropy method for lossless source coding with side information is described in detail. The text has been carefully revised, and typos have been corrected. Finally, the bibliography has been updated by including references which have been published since the original publication.

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