Information Theory, Inference and Learning Algorithms Hardback - Tapa blanda

MacKay, David J. C.

9780521642989: Information Theory, Inference and Learning Algorithms Hardback

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ISBN 10: 0521642981 ISBN 13: 9780521642989

Editorial: Cambridge University Press, 2003

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This exciting and entertaining textbook is ideal for courses in information, communication and coding. It is an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, data mining, financial engineering and machine learning.

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'This is an extraordinary and important book, generous with insight and rich with detail in statistics, information theory, and probabilistic modeling across a wide swathe of standard, creatively original, and delightfully quirky topics. David MacKay is an uncompromisingly lucid thinker, from whom students, faculty and practitioners all can learn.' Peter Dayan and Zoubin Ghahramani, Gatsby Computational Neuroscience Unit, University College, London

'This is primarily an excellent textbook in the areas of information theory, Bayesian inference and learning algorithms. Undergraduates and postgraduates students will find it extremely useful for gaining insight into these topics; however, the book also serves as a valuable reference for researchers in these areas. Both sets of readers should find the book enjoyable and highly useful.' David Saad, Aston University

'An utterly original book that shows the connections between such disparate fields as information theory and coding, inference, and statistical physics.' Dave Forney, Massachusetts Institute of Technology

'An instant classic, covering everything from Shannon's fundamental theorems to the postmodern theory of LDPC codes. You'll want two copies of this astonishing book, one for the office and one for the fireside at home.' Bob McEliece, California Institute of Technology

'... a quite remarkable work ... the treatment is specially valuable because the author has made it completely up-to-date ... this magnificent piece of work is valuable in introducing a new integrated viewpoint, and it is clearly an admirable basis for taught courses, as well as for self-study and reference. I am very glad to have it on my shelves.' Robotica

'With its breadth, accessibility and handsome design, this book should prove to be quite popular. Highly recommended as a primer for students with no background in coding theory, the set of chapters on error correcting codes are an excellent brief introduction to the elements of modern sparse graph codes: LDPC, turbo, repeat-accumulate and fountain codes are described clearly and succinctly.' IEEE Transactions on Information Theory

Product 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.

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EditorialCambridge University Press
A�o de publicaci�n2003
ISBN 10 0521642981
ISBN 13 9780521642989
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de p�ginas642

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9780521670517: Information Theory Inference And Learning Algorithms

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Information Theory, Inference and Learning Algorithms

MacKay, David

Publicado por Cambridge University Press, 2003

ISBN 10: 0521642981 ISBN 13: 9780521642989

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Information Theory, Inference and Learning Algorithms

MacKay, David J. C.

Publicado por Cambridge University Press, 2003

ISBN 10: 0521642981 ISBN 13: 9780521642989

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INFORMATION THEORY INFERENCE & LEARNING

MACKAY, DAVID

Publicado por Cambridge University Press, 2003

ISBN 10: 0521642981 ISBN 13: 9780521642989

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Information Theory, Inference

MacKay, David J. C.

Publicado por Cambridge University Press, 2003

ISBN 10: 0521642981 ISBN 13: 9780521642989

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Condici�n: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. N� de ref. del art�culo: 00087035343

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Information Theory, Inference and Learning Algorithms

David J. C. MacKay

Publicado por Cambridge University Press, 2003

ISBN 10: 0521642981 ISBN 13: 9780521642989

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Condici�n: New. 2003. Sixth Printing 2007. Hardcover. Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering. Num Pages: 640 pages, 1 colour illus. 40 tables 390 exercises. BIC Classification: PH; TJ; UM; UY. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 251 x 192 x 35. Weight in Grams: 1500. 640 pages, 1 colour illus. 40 tables 390 exercises. Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: PH; TJ; UM; UY. Dimension: 251 x 192 x 35. Weight: 1494. . . . . . N� de ref. del art�culo: V9780521642989

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