Elements of Information Theory

The latest edition of this classic is updated with new problem sets and material

The Second Edition of this fundamental textbook maintains the book’s tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.

All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points.

The Second Edition features:

* Chapters reorganized to improve teaching

* 200 new problems

* New material on source coding, portfolio theory, and feedback capacity

* Updated references

Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.

Table of Contents

1 Introduction and Preview

2 Entropy, Relative Entropy, and Mutual Information

3 Asymptotic Equipartition Property

4 Entropy Rates of a Stochastic Process

5 Data Compression

6 Gambling and Data Compression

7 Channel Capacity

8 Differential Entropy

9 Gaussian Channel

10 Rate Distortion Theory

11 Information Theory and Statistics

12 Maximum Entropy

13 Universal Source Coding

14 Kolmogorov Complexity

15 Network Information Theory

16 Information Theory and Portfolio Theory

17 Inequalities in Information Theory

About the Authors

Thomas M. Cover, is Professor in the departments of electrical engineering and statistics, Stanford University. A recipient of the 1991 IEEE Claude E. Shannon Award, Dr. Cover is a past president of the IEEE Information Theory Society, a Fellow of the IEEE and the Institute of Mathematical Statistics, and a member of the National Academy of Engineering and the American Academy of Arts and Science. He has authored more than 100 technical papers and is coeditor of Open Problems in Communication and Computation.

Joy A. Thomas, is the Chief Scientist at Stratify, Inc., a Silicon Valley start-up specializing in organizing unstructured information. After receiving his PhD at Stanford, Dr. Thomas spent more than nine years at the IBM T. J. Watson Research Center in Yorktown Heights, New York. Dr. Thomas is a recipient of the IEEE Charles LeGeyt Fortescue Fellowship.