Concentration Of Measure Inequalities In Information Theory Communications And Coding Thirdedition
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Concentration of Measure Inequalities in Information Theory, Communications, and Coding: ThirdEdition
Author: Maxim Raginsky
language: en
Publisher: Foundations and Trends (R) in Communications and Information Theory
Release Date: 2018-12-18
This book 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.
Concentration Inequalities
Author: Stéphane Boucheron
language: en
Publisher: Oxford University Press
Release Date: 2013-02-07
Describes the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented.
Probabilistic Methods for Algorithmic Discrete Mathematics
Author: Michel Habib
language: en
Publisher: Springer Science & Business Media
Release Date: 1998-08-19
The book gives an accessible account of modern pro- babilistic methods for analyzing combinatorial structures and algorithms. Each topic is approached in a didactic manner but the most recent developments are linked to the basic ma- terial. Extensive lists of references and a detailed index will make this a useful guide for graduate students and researchers. Special features included: - a simple treatment of Talagrand inequalities and their applications - an overview and many carefully worked out examples of the probabilistic analysis of combinatorial algorithms - a discussion of the "exact simulation" algorithm (in the context of Markov Chain Monte Carlo Methods) - a general method for finding asymptotically optimal or near optimal graph colouring, showing how the probabilistic method may be fine-tuned to explit the structure of the underlying graph - a succinct treatment of randomized algorithms and derandomization techniques