Entropy and Information Theory
Description
This book is an updated version of the information theory classic, first published in 1990. About one-third of the book is devoted to Shannon source and channel coding theorems; the remainder addresses sources, channels, and codes and on information and distortion measures and their properties.
New in this edition:
- Expanded treatment of stationary or sliding-block codes and their relations to traditional block codes
- Expanded discussion of results from ergodic theory relevant to information theory
- Expanded treatment of B-processes -- processes formed by stationary coding memoryless sources
- New material on trading off information and distortion, including the Marton inequality
- New material on the properties of optimal and asymptotically optimal source codes
- New material on the relationships of source coding and rate-constrained simulation or modeling of random processes
Significant material not covered in other information theory texts includes stationary/sliding-block codes, a geometric view of information theory provided by process distance measures, and general Shannon coding theorems for asymptotic mean stationary sources, which may be neither ergodic nor stationary, and d-bar continuous channels.
Reviews / Votes
From the book reviews:
"This book is the second edition of the classic 1990 text . and inherits much of the structure and all of the virtues of the original. . this is a deep and important book, which would reward further study as the focus of a reading group or graduate course, and comes enthusiastically recommended." (Oliver Johnson, Mathematical Reviews, October, 2014)
"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)
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