Entropy and Information Theory [Paperback]

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.

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.

Preface.- Introduction.- Information Sources.- Pair Processes: Channels, Codes, and Couplings.- Entropy.- The Entropy Ergodic Theorem.- Distortion and Approximation.- Distortion and Entropy.- Relative Entropy.- Information Rates.- Distortion vs. Rate.- Relative Entropy Rates.- Ergodic Theorems for Densitilă°