Entropy and Information Theory

Entropy and Information Theory

by Robert M. Gray
     
 

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This fully updated new edition of the classic work on information theory presents a detailed analysis of Shannon-source and channel-coding theorems, before moving on to address sources, channels, codes and the properties of information and distortion measures.

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Overview

This fully updated new edition of the classic work on information theory presents a detailed analysis of Shannon-source and channel-coding theorems, before moving on to address sources, channels, codes and the properties of information and distortion measures.

Editorial Reviews

From the Publisher
From the reviews of the second edition:

“In Entropy and Information Theory Robert Gray offers an excellent text to stimulate research in this field. … Entropy and Information Theory is highly recommended as essential reading to academics and researchers in the field, especially to engineers interested in the mathematical aspects and mathematicians interested in the engineering applications. … it will contribute to further synergy between the two fields and the deepening of research efforts.” (Ina Fourie, Online Information Review, Vol. 36 (3), 2012)

“The book offers interesting and very important information about the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels. The main goal is a general development of Shannon’s mathematical theory of communication for single-user systems. … The author manages to balance the practice with the theory, every chapter is very well structured and has high-value content.” (Nicolae Constantinescu, Zentralblatt MATH, Vol. 1216, 2011)

Booknews
On the theory of probabilistic information measures and their application to coding theorems for general information sources, noisy channels, and block and sliding block codes. The goal is a general development of Shannon's mathematical theory of communication, but much of the book is devoted to the tools and methods required to prove the Shannon coding theorems. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9781441979698
Publisher:
Springer US
Publication date:
02/03/2011
Edition description:
2nd ed. 2011
Pages:
409
Product dimensions:
6.40(w) x 9.30(h) x 1.30(d)

Meet the Author

Robert M. Gray is the Alcatel-Lucent Technologies Professor of Communications and Networking in the School of Engineering and Professor of Electrical Engineering at Stanford University. For over four decades he has done research, taught, and published in the areas of information theory and statistical signal processing. He is a Fellow of the IEEE and the Institute for Mathematical Statistics. He has won several professional awards, including a Guggenheim Fellowship, the Society Award and Education Award of the IEEE Signal Processing Society, the Claude E. Shannon Award from the IEEE Information Theory Society, the Jack S. Kilby Signal Processing Medal, Centennial Medal, and Third Millennium Medal from the IEEE, and a Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM). He is a member of the National Academy of Engineering.

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