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Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms


  • Date Published: September 2003
  • availability: In stock
  • format: Hardback
  • isbn: 9780521642989
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About the Authors
  • 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.

    • 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

    '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

    'This is primarily an excellent textbook in the areas of information theory, Bayesian inference and learning algorithms. Undergraduates and postgraduates students will find it extremely useful for gaining insight into these topics; however, the book also serves as a valuable reference for researchers in these areas. Both sets of readers should find the book enjoyable and highly useful.' David Saad, Aston University

    '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

    '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

    '… a quite remarkable work … the treatment is specially valuable because the author has made it completely up-to-date … this magnificent piece of work is valuable in introducing a new integrated viewpoint, and it is clearly an admirable basis for taught courses, as well as for self-study and reference. I am very glad to have it on my shelves.' Robotica

    'With its breadth, accessibility and handsome design, this book should prove to be quite popular. Highly recommended as a primer for students with no background in coding theory, the set of chapters on error correcting codes are an excellent brief introduction to the elements of modern sparse graph codes: LDPC, turbo, repeat-accumulate and fountain codes are described clearly and succinctly.' IEEE Transactions on Information Theory

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    Customer reviews

    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


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    Product details

    • Date Published: September 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

  • Resources for

    Information Theory, Inference and Learning Algorithms

    David J. C. MacKay

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  • Instructors have used or reviewed this title for the following courses

    • Applied Mathematics
    • Information theory and convolutional channel coding
  • Author

    David J. C. MacKay, University of Cambridge

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