Information Theory, Inference and Learning Algorithms (Hardcover)
Language: English
Published by Cambridge University Press, Cambridge, 2003
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
Condition: New
£ 71.99
Quantity: 1 available
Add to basketItem description from seller
Hardcover. Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook 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. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in 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, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning. This textbook covers Shannon's theory of information as well as neural networks and probabilistic data modelling. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9780521642989
- Title
- Information Theory, Inference and Learning Algorithms (Hardcover)
- Author
- David J.C. MacKay
- Publisher
- Cambridge University Press, Cambridge
- Publication year
- 2003
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0521642981
- ISBN 13
- 9780521642989
"Synopsis" may belong to another edition of this title.
From the Inside Flap
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.
-
Zoubin Ghahramani and Peter Dayan
Gatsby Computational Neuroscience Unit, University College London.
An utterly original book that shows the connections between such disparate fields as information theory and coding, inference, and statistical physics.
Dave Forney, M.I.T.
"About the title" may belong to another edition of this title.
CitiRetail
Stevenage, United Kingdom
AbeBooks seller since June 29, 2022
Shipping rates from United Kingdom to U.S.A.
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