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  • Book
  • © 2011

Entropy and Information Theory

Authors:

  • New edition of classic text
  • Important engineering applications of performance bounds and code design for communication systems
  • Distinguished author
  • Includes supplementary material: sn.pub/extras

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxvii
  2. Information Sources

    • Robert M. Gray
    Pages 1-19
  3. Entropy

    • Robert M. Gray
    Pages 61-95
  4. The Entropy Ergodic Theorem

    • Robert M. Gray
    Pages 97-115
  5. Distortion and Approximation

    • Robert M. Gray
    Pages 117-146
  6. Distortion and Entropy

    • Robert M. Gray
    Pages 147-171
  7. Relative Entropy

    • Robert M. Gray
    Pages 173-218
  8. Information Rates

    • Robert M. Gray
    Pages 219-236
  9. Distortion and Information

    • Robert M. Gray
    Pages 237-263
  10. Relative Entropy Rates

    • Robert M. Gray
    Pages 265-279
  11. Ergodic Theorems for Densities

    • Robert M. Gray
    Pages 281-294
  12. Source Coding Theorems

    • Robert M. Gray
    Pages 295-334
  13. Properties of Good Source Codes

    • Robert M. Gray
    Pages 335-357
  14. Coding for Noisy Channels

    • Robert M. Gray
    Pages 359-394
  15. Back Matter

    Pages 395-409

About this book

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

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)

Authors and Affiliations

  • Dept. Electrical Engineering, Stanford University, Stanford, USA

    Robert M. Gray

About 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.

Bibliographic Information

Buy it now

Buying options

eBook USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access