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.Read more
- Readable, fun and enthusiastic introduction to a dynamic and exciting field
- Covers theory and applications in tandem, including discussion of state-of-the-art codes used in data compression, error correction and learning; and Bayesian models and Monte Carlo methods
- Contains lots of worked examples and exercises, many of which have full solutions in the book
Reviews & endorsements
"...a valuable reference...enjoyable and highly useful."
American ScientistSee more reviews
"...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."
"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."
"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."
"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
17th Oct 2016 by Sajidsaleem
The Best Book on the subject. I am using it as reference for Masters class i am teaching.
Review was not posted due to profanity×
- Date Published: October 2003
- format: Hardback
- isbn: 9780521642989
- length: 640 pages
- dimensions: 254 x 195 x 34 mm
- weight: 1.56kg
- contains: 1 colour illus. 40 tables 390 exercises
- availability: In stock
Table of Contents
1. Introduction to information theory
2. Probability, entropy and inference
3. More about inference
Part I. Data Compression:
4. The source coding theorem
5. Symbol codes
6. Stream codes
7. Codes for integers
Part II. Noisy-Channel Coding:
8. Dependent random variables
9. Communication over a noisy channel
10. The noisy-channel coding theorem
11. Error-correcting codes and real channels
Part III. Further Topics in Information Theory:
12. Hash codes
13. Binary codes
14. Very good linear codes exist
15. Further exercises on information theory
16. Message passing
17. Constrained noiseless channels
18. Crosswords and codebreaking
19. Why have sex? Information acquisition and evolution
Part IV. Probabilities and Inference:
20. An example inference task: clustering
21. Exact inference by complete enumeration
22. Maximum likelihood and clustering
23. Useful probability distributions
24. Exact marginalization
25. Exact marginalization in trellises
26. Exact marginalization in graphs
27. Laplace's method
28. Model comparison and Occam's razor
29. Monte Carlo methods
30. Efficient Monte Carlo methods
31. Ising models
32. Exact Monte Carlo sampling
33. Variational methods
34. Independent component analysis
35. Random inference topics
36. Decision theory
37. Bayesian inference and sampling theory
Part V. Neural Networks:
38. Introduction to neural networks
39. The single neuron as a classifier
40. Capacity of a single neuron
41. Learning as inference
42. Hopfield networks
43. Boltzmann machines
44. Supervised learning in multilayer networks
45. Gaussian processes
Part VI. Sparse Graph Codes
47. Low-density parity-check codes
48. Convolutional codes and turbo codes
49. Repeat-accumulate codes
50. Digital fountain codes
Part VII. Appendices: A. Notation
B. Some physics
C. Some mathematics
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