Information Theory, Inference and Learning Algorithms
Language: English
Published by Cambridge University Press Sep 2003, 2003
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 140.97
Quantity: 2 available
Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9780521642989
- Title
- Information Theory, Inference and Learning Algorithms
- Author
- David J. C. MacKay
- Publisher
- Cambridge University Press Sep 2003
- Publication year
- 2003
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0521642981
- ISBN 13
- 9780521642989
- Item weight
- 1,578 grams
- Dimensions
- 260x208x39 mm
"Synopsis" may belong to another edition of this title.
Review
American Scientist
"...an impressive book, intended as a class text on the subject of the title but having the character and robustness of a focused encyclopedia. The presentation is finely detailed, well documented, and stocked with artistic flourishes."
Mathematical Reviews
"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions."
Choice
"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
"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
"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
"An excellent textbook in the areas of infomation theory, Bayesian inference and learning alorithms. Undergraduate and post-graduate students will find it extremely useful for gaining insight into these topics."
REDNOVA
"Most of the theories are accompanied by motivations, and explanations with the corresponding examples...the book achieves its goal of being a good textbook on information theory."
ACM SIGACT News
"About the title" may belong to another edition of this title.
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