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Entropy and Information Theory

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Book Details:

Publisher:Springer
Series: Springer
Author:Robert M. Gray
Edition:1
ISBN-10:0387973710
ISBN-13:9780387973715
Pages:332
Published:Sep 04 1990
Posted:Nov 19 2014
Language:English
Book format:PDF
Book size:1.26 MB

Book Description:

This book is devoted to the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels. The eventual goal is a general development of Shannon's mathematical theory of communication, but much of the space is devoted to the tools and methods required to prove the Shannon coding theorems. These tools form an area common to ergodic theory and information theory and comprise several quantitative notions of the information in random variables, random processes, and dynamical systems. Examples are entropy, mutual information, conditional entropy, conditional information, and discrimination or relative entropy, along with the limiting normalized versions of these quantities such as entropy rate and information rate. Much of the book is concerned with their properties, especially the long term asymptotic behavior of sample information and expected information. This is the only up-to-date treatment of traditional information theory emphasizing ergodic theory.

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