Lecture Notes on Network Information Theory
by Abbas El Gamal, Young-Han Kim
Publisher: arXiv 2010
Number of pages: 640
Network information theory deals with the fundamental limits on information flow in networks and optimal coding techniques and protocols that achieve these limits. This set of lecture notes aims to provide a broad coverage of key results, techniques, and open problems in network information theory.
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by Inder Jeet Taneja - Universidade Federal de Santa Catarina
Contents: Shannon's Entropy; Information and Divergence Measures; Entropy-Type Measures; Generalized Information and Divergence Measures; M-Dimensional Divergence Measures and Their Generalizations; Unified (r,s)-Multivariate Entropies; etc.
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.
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The book presents the strongest possible version of Gödel's incompleteness theorem, using an information-theoretic approach based on the size of computer programs. The author tried to present the material in the most direct fashion possible.
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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.