Entropy and Information Theory
by Robert M. Gray
Publisher: Springer 2008
Number of pages: 313
This book is devoted to the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels. The eventual goal is a general development of Shannon's mathematical theory of communication, but much of the space is devoted to the tools and methods required to prove the Shannon coding theorems. This is the only up-to-date treatment of traditional information theory emphasizing ergodic theory.
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by Robert M. Gray - Information Systems Laboratory
The conditional rate-distortion function has proved useful in source coding problems involving the possession of side information. This book represents an early work on conditional rate distortion functions and related theory.
by David Feldman - College of the Atlantic
This e-book is a brief tutorial on information theory, excess entropy and statistical complexity. From the table of contents: Background in Information Theory; Entropy Density and Excess Entropy; Computational Mechanics.
by Claude Shannon
Shannon presents results previously found nowhere else, and today many professors refer to it as the best exposition on the subject of the mathematical limits on communication. It laid the modern foundations for what is now coined Information Theory.
Data compression is useful in some situations because 'compressed data' will save time (in reading and on transmission) and space if compared to the unencoded information it represent. In this book, we describe the decompressor first.