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Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 9780521642989
Category : Computers
Languages : en
Pages : 694
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Book Description
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, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 9780521642989
Category : Computers
Languages : en
Pages : 694
Get Book Here
Book Description
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, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Author: Robert M. Gray
Publisher: Springer Science & Business Media
ISBN: 1475739826
Category : Computers
Languages : en
Pages : 346
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Book Description
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.
Author: Thomas M. Cover
Publisher: John Wiley & Sons
ISBN: 1118585771
Category : Computers
Languages : en
Pages : 788
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Book Description
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.
Author: Imre Csiszár
Publisher: Elsevier
ISBN: 1483281574
Category : Mathematics
Languages : en
Pages : 465
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Book Description
Information Theory: Coding Theorems for Discrete Memoryless Systems presents mathematical models that involve independent random variables with finite range. This three-chapter text specifically describes the characteristic phenomena of information theory. Chapter 1 deals with information measures in simple coding problems, with emphasis on some formal properties of Shannon's information and the non-block source coding. Chapter 2 describes the properties and practical aspects of the two-terminal systems. This chapter also examines the noisy channel coding problem, the computation of channel capacity, and the arbitrarily varying channels. Chapter 3 looks into the theory and practicality of multi-terminal systems. This book is intended primarily for graduate students and research workers in mathematics, electrical engineering, and computer science.
Author: James V Stone
Publisher: Packt Publishing Ltd
ISBN: 183702684X
Category : Computers
Languages : en
Pages : 294
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Book Description
Learn the fundamentals of information theory, including entropy, coding, and data compression, while exploring advanced topics like transfer entropy, thermodynamics, and real-world applications. Key Features A clear blend of foundational theory and advanced topics suitable for various expertise levels A focus on practical examples to complement theoretical concepts and enhance comprehension Comprehensive coverage of applications, including data compression, thermodynamics, and biology Book DescriptionThis book offers a comprehensive journey through the fascinating world of information theory, beginning with the fundamental question: what is information? Early chapters introduce key concepts like entropy, binary representation, and data compression, providing a clear and accessible foundation. Readers explore Shannon's source coding theorem and practical tools like Huffman coding to understand how information is quantified and optimized. Building on these basics, the book delves into advanced topics such as the noisy channel coding theorem, mutual information, and error correction techniques. It examines entropy in continuous systems, channel capacity, and rate-distortion theory, making complex ideas accessible through real-world examples. Connections between information and thermodynamics are also explored, including Maxwell’s Demon, the Landauer Limit, and the second law of thermodynamics. The final chapters tie information theory to biology and artificial intelligence, investigating its role in evolution, the human genome, and brain computation. With practical examples throughout, this book balances theoretical depth with hands-on learning, making it an essential resource for mastering information theory. A basic mathematical foundation will be beneficial but is not required to engage with the material.What you will learn Understand the core concepts of information theory Analyze entropy in discrete and continuous systems Explore Shannon's source and channel coding theorems Apply Huffman coding and data compression techniques Examine mutual information and its significance Relate thermodynamic entropy to information theory Who this book is for This book is perfect for students, engineers, and researchers in computer science, electrical engineering, physics, and related fields. A basic mathematical foundation will enhance understanding and ensure readers can fully grasp the concepts and their practical applications.
Author: Abbas El Gamal
Publisher: Cambridge University Press
ISBN: 1139503146
Category : Technology & Engineering
Languages : en
Pages : 666
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Book Description
This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.
Author: Mark Wilde
Publisher: Cambridge University Press
ISBN: 1107034256
Category : Computers
Languages : en
Pages : 673
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Book Description
A self-contained, graduate-level textbook that develops from scratch classical results as well as advances of the past decade.
Author: Te Sun Han
Publisher: Springer Science & Business Media
ISBN: 3662120666
Category : Mathematics
Languages : en
Pages : 552
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Book Description
From the reviews: "This book nicely complements the existing literature on information and coding theory by concentrating on arbitrary nonstationary and/or nonergodic sources and channels with arbitrarily large alphabets. Even with such generality the authors have managed to successfully reach a highly unconventional but very fertile exposition rendering new insights into many problems." -- MATHEMATICAL REVIEWS
Author: Robert B. Ash
Publisher:
ISBN:
Category : Information theory
Languages : en
Pages : 339
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Book Description
Author: D.C. Hankerson
Publisher: CRC Press
ISBN: 9781584883135
Category : Mathematics
Languages : en
Pages : 394
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Book Description
An effective blend of carefully explained theory and practical applications, this text imparts the fundamentals of both information theory and data compression. Although the two topics are related, this unique text allows either topic to be presented independently, and it was specifically designed so that the data compression section requires no prior knowledge of information theory. The treatment of information theory, while theoretical and abstract, is quite elementary, making this text less daunting than many others. After presenting the fundamental definitions and results of the theory, the authors then apply the theory to memoryless, discrete channels with zeroth-order, one-state sources. The chapters on data compression acquaint students with a myriad of lossless compression methods and then introduce two lossy compression methods. Students emerge from this study competent in a wide range of techniques. The authors' presentation is highly practical but includes some important proofs, either in the text or in the exercises, so instructors can, if they choose, place more emphasis on the mathematics. Introduction to Information Theory and Data Compression, Second Edition is ideally suited for an upper-level or graduate course for students in mathematics, engineering, and computer science. Features: Expanded discussion of the historical and theoretical basis of information theory that builds a firm, intuitive grasp of the subject Reorganization of theoretical results along with new exercises, ranging from the routine to the more difficult, that reinforce students' ability to apply the definitions and results in specific situations. Simplified treatment of the algorithm(s) of Gallager and Knuth Discussion of the information rate of a code and the trade-off between error correction and information rate Treatment of probabilistic finite state source automata, including basic results, examples, references, and exercises Octave and MATLAB image compression codes included in an appendix for use with the exercises and projects involving transform methods Supplementary materials, including software, available for download from the authors' Web site at www.dms.auburn.edu/compression
