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.
Customer Reviews:
Avg. Customer Rating: 4.5 / 5.0
A Bayesian View: Excellent Topics, Exposition and Coverage:
I am reviewing David MacKay's `Information Theory, Inference, and Learning Algorithms, but I haven't yet read completely. It will be years before I finish it, since it contains the material for several advanced undergraduate or graduate courses. However, it is already on my list of favorite texts and references. It is a book I will keep going back to time after time, but don't take my word for it. According to the back cover, Bob McEliece, the author of a 1977 classic on information theory recommends you... more info
pretty much indispensible:
This is an unqualified classic, to shelve with the likes of 'Structure and Interpretation of Computer Programs', 'Concrete Mathematics' and 'Mathematical Methods of Classical Mechanics'. If you are involved with, or interested in, high-end data analytics, then you _need_ this. However 'high-end data analytics' does not even begin to do the book justice, so let me try again. This is a magnificient compendium of fascinating stuff presented in a coherent information-theoretic framework. It covers... more info
Outstanding book, especially for statisticians:
I find it interesting that most of the people reviewing this book seem to be reviewing it as they would any other information theory textbook. Such a review, whether positive or critical, could not hope to give a complete picture of what this text actually is. There are many books on information theory, but what makes this book unique (and in my opinion what makes it so outstanding) is the way it integrates information theory with statistical inference. The book covers topics including coding theory,... more info
Great wish it had more n option inverse problems:
This is fantastic book. Really takes an intuitive approach to the material. The explanation of occam's razor is worth the price of the whole book. Highly recommended.
