Description
Springer Entropy and Information Theory by Robert M. Gray
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. _x000D__x000D_New in this edition:_x000D__x000D__x000D__x000D_Expanded treatment of stationary or sliding-block codes and their relations to traditional block codes_x000D_Expanded discussion of results from ergodic theory relevant to information theory_x000D_Expanded treatment of B-processes -- processes formed by stationary coding memoryless sources_x000D_New material on trading off information and distortion, including the Marton inequality_x000D_New material on the properties of optimal and asymptotically optimal source codes_x000D_New material on the relationships of source coding and rate-constrained simulation or modeling of random processes_x000D__x000D_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._x000D_ Table of contents : - _x000D_
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 Densities.- Source Coding Theorems.- Coding for Noisy Channels.- Bibliography.- References.- Index_x000D_