The latest edition of this classic is updated with new problem sets and material Elements of Information Theory, 2nd Edition Thomas M. Cover, Joy A. Thomas ISBN: 0-471-24195-4 Hardcover 776 pages July 2006 Description: Table of Contents: Author Information: The latest edition of … Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. Problem sets and a telegraphic summary at the end of each chapter further assist readers. This is a dummy description. 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.
Download Product Flyer is to download PDF in new tab. Buy Elements of Information Theory 91 edition (9780471062592) by Thomas M. Cover and Joy A. Thomas for up to 90% off at Textbooks.com. $129.00 Download Product Flyer is to download PDF in new tab. Clear explanations, nice graphical illustrations, and illuminating mathematical derivations make the book particularly useful as a textbook on information theory." This book does NOT treat Information Theory as a subset of reliable communication theory; therefore, the book is NOT written as a competitor for Gallager's classic text. "As expected, the quality of exposition continues to be a high point of the book. The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the books tradition of clear, thought-provoking instruction. This is a dummy description. This is a dummy description. Cover and Thomas' book "Elements of Information Theory" is written for the reader who is interested in these eclectic and exciting applications of Information Theory. 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. 2.4 Relationship Between Entropy and Mutual Information 202.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information 224.3 Example: Entropy Rate of a Random Walk on a Weighted Graph 785.5 Kraft Inequality for Uniquely Decodable Codes 1155.10 Competitive Optimality of the Shannon Code 1305.11 Generation of Discrete Distributions from Fair Coins 1346.6 Gambling Estimate of the Entropy of English 1737.1.2 Noisy Channel with Nonoverlapping Outputs 1857.9 Fano’s Inequality and the Converse to the Coding Theorem 2067.10 Equality in the Converse to the Channel Coding Theorem 2088.3 Relation of Differential Entropy to Discrete Entropy 2478.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information 2529.2 Converse to the Coding Theorem for Gaussian Channels 26810.3 Calculation of the Rate Distortion Function 30710.3.3 Simultaneous Description of Independent Gaussian Random Variables 31210.5 Achievability of the Rate Distortion Function 31810.6 Strongly Typical Sequences and Rate Distortion 32510.7 Characterization of the Rate Distortion Function 32910.8 Computation of Channel Capacity and the Rate Distortion Function 33211.10 Fisher Information and the Cramér–Rao Inequality 39213.5.2 Optimality of Tree-Structured Lempel–Ziv Compression 44814.2 Kolmogorov Complexity: Definitions and Examples 46614.5 Algorithmically Random and Incompressible Sequences 47614.11 Kolmogorov Complexity and Universal Probability 49015.3.1 Achievability of the Capacity Region for the Multiple-Access Channel 53015.3.2 Comments on the Capacity Region for the Multiple-Access Channel 53215.3.3 Convexity of the Capacity Region of the Multiple-Access Channel 53415.3.4 Converse for the Multiple-Access Channel 53815.4.1 Achievability of the Slepian–Wolf Theorem 55115.5 Duality Between Slepian–Wolf Encoding and Multiple-Access Channels 55815.6.3 Capacity Region for the Degraded Broadcast Channel 56516.2 Kuhn–Tucker Characterization of the Log-Optimal Portfolio 61716.3 Asymptotic Optimality of the Log-Optimal Portfolio 61916.6 Competitive Optimality of the Log-Optimal Portfolio 62716.8 Shannon–McMillan–Breiman Theorem (General AEP) 64417.8 Entropy Power Inequality and Brunn–Minkowski Inequality 674
* 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. Quantity: Download Product Flyer is to download PDF in new tab.
Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.
The authors provide readers with a solid understanding of the underlying theory and applications.
CONTENTS ix 10.8 Computation of Channel Capacity and the Rate Distortion Function 332 Summary 335 Problems 336 Historical Notes 345 11 Information Theory and Statistics 347 11.1 Method of Types 347 11.2 Law of Large Numbers 355 11.3 Universal Source Coding 357 11.4 Large Deviation Theory 360 11.5 Examples of Sanov’s Theorem 364
Elements of Information Theory, Second Edition, 2006 .
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