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Information Theory, Inference and Learning Algorithms
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independ...
A First Course in Information Theory
An introduction to information theory for discrete random variables. Classical topics and fundamental tools are presented along with three selected advanced topics. Yeung (Chinese U. of Hong Kong) presents chapters on information measures, zero-error data compression, weak and strong typicality, the I-measure, Markov structures, channel capacity, rate distortion theory, Blahut-Arimoto algorithms, information inequalities, and Shannon-type inequalities. The advanced topics included are single-source network coding, multi-source network coding, and entropy and groups. Annotation copyrighted by Book News, Inc., Portland, OR.
Mathematical Foundations of Information Theory
First comprehensive introduction to information theory explores the work of Shannon, McMillan, Feinstein, and Khinchin. Topics include the entropy concept in probability theory, fundamental theorems, and other subjects. 1957 edition.
Information Theory
DIVAnalysis of channel models and proof of coding theorems; study of specific coding systems; and study of statistical properties of information sources. Sixty problems, with solutions. Advanced undergraduate to graduate level. /div
Entropy and Information Theory
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. These tools form an area common to ergodic theory and information theory and comprise several quantitative notions of the information in random variables, random processes, and dynamical systems. Examples are entropy, mutual information, conditional entropy, conditional information, and discrimination or relative entropy, along with the limiting normalized versions of these quantities such as entropy rate and information rate. Much of the book is concerned with their properties, especially the long term asymptotic behavior of sample information and expected information. This is the only up-to-date treatment of traditional information theory emphasizing ergodic theory.
An Introduction to Information Theory
Graduate-level study for engineering students presents elements of modern probability theory, elements of information theory with emphasis on its basic roots in probability theory and elements of coding theory. Emphasis is on such basic concepts as sets, sample space, random variables, information measure, and capacity. Many reference tables and extensive bibliography. 1961 edition.
Information Theory
Students of electrical engineering or applied mathematics can find no clearer presentation of the principles of information theory than this excellent introduction. After explaining the nature of information theory and its problems, the author examines a variety of important topics: information theory of discrete systems; properties of continuous signals; ergodic ensembles and random noise; entropy of continuous distributions; the transmission of information in band-limited systems having a continuous range of values; an introduction to the use of signal space; information theory aspects of modulation and noise reduction; and linear correlation, filtering, and prediction. Numerous problems appear throughout the text, many with complete solutions. 1953 ed.
Information Theory and Statistics
Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.
Elements of Information Theory
The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. 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. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * 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.
An Introduction to Information Theory
Covers encoding and binary digits, entropy, language and meaning, efficient encoding and the noisy channel, and explores ways in which information theory relates to physics, cybernetics, psychology, and art. 1980 edition.
