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Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms

$84.00

  • Date Published: October 2003
  • availability: In stock
  • format: Hardback
  • isbn: 9780521642989

$84.00
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  • 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.

    • 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
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    Reviews & endorsements

    "...a valuable reference...enjoyable 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 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

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    Product details

    • Date Published: October 2003
    • format: Hardback
    • isbn: 9780521642989
    • length: 640 pages
    • dimensions: 254 x 195 x 34 mm
    • weight: 1.525kg
    • 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
    46. Deconvolution
    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
    Bibliography
    Index.

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    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

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  • Author

    David J. C. MacKay, University of Cambridge

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