Features
- Cover Type: Hard Cover with 550 pages
- Published by: Cambridge University Press
- Edition: 1st Edition June 15, 2002
- Written in: English
- ISBN 10 Number: 0521642981
- ISBN 13 Number: 978-0521642989
-
Book Dimensions:
10.5 x 7.7 x 1.4 inches
- Weighs: 3.3 pounds
Product Review
"a valuable referenceenjoyable and highly useful."
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 amazing book, one for the office and one for the fireside at home."
Bob McEliece, California Institute of Technology
"An great 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 examplesthe book achieves its goal of being a good textbook on information theory."
ACM SIGACT News
Product Description
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.
Reader Reviews
I have used this to get a good background in the topics covered, especially inference theory, and in general I found it to be great book which fills a market gap. The only sins I see are sins of omission. I personally would have enjoyed seeing a more task driven organization. I seem to need these methods periodically but I never seem to need the same method twice. Also, many of the techniques are heavily iterative, i.e., monte carlo, neural networks, etc. This is fine but much of what I do is in the context of simulations where 100,000 step iterative methods don't work so well because of resource constraints. Historically, that has been the problem with many of these methods. They are useful for relatively small domains but don't necessarily work that well for "real" problems. That is probably why more task oriented books are not available. Of course the author is following the outline of the current research into the subject manner which in turn is largely determined by "interesting" and "doable" problems. The real progess in this field will come when the problems are formulated more by what is needed in the nontraditional domains of application. A good example of a useful compression (and identification in some cases) technique that is not covered is Principal Component Analysis. Technically, it is in none of the technique domains covered in this book, but it would have been nice to see some of the methods in the book compared with PCA. The author does make the statement at one point that image recognition is an interesting problem for which the method being discussed at the time is used. Nevertheless, this is a great overview of the subject manner and is very entertaining. That in the long run probably explains the problem: it is a textbook.
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