New book on Information Theory and State-of-the-art Coding Theory

Discussion in 'VOIP' started by David MacKay, Nov 5, 2003.

  1. David MacKay

    David MacKay Guest

    New from Cambridge University Press:
    'Information Theory, Inference and Learning Algorithms'
    by David J.C. MacKay

    -------------------------------------------------------------------
    "An instant classic... 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
    -------------------------------------------------------------------

    Hardback, 640 pages.

    Also available for free onscreen viewing from
    http://www.inference.phy.cam.ac.uk/mackay/itila/

    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. Correlated 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: codes for efficient information retrieval;
    13. Binary codes;
    14. Very good linear codes exist;
    15. Further exercises on information theory;
    16. Message passing;
    17. Communication over constrained noiseless channels;
    18. An aside: 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 and latent variable modelling;
    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.

    You can obtain more details from:
    http://titles.cambridge.org/catalogue.asp?isbn=0521642981
    And lecturers can request inspection copies at:
    http://uk.cambridge.org/textbooks

    *If you are based in the USA, Canada or Mexico,*
    please go to:
    http://us.cambridge.org/titles/catalogue.asp?isbn=0521642981
    Lecturer's exam copies can be requested at:
    http://us.cambridge.org/information/examcopy.htm

    From the back cover
    -------------------

    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.

    The entire book may be previewed online at
    http://www.inference.phy.cam.ac.uk/mackay/itila/
     
    David MacKay, Nov 5, 2003
    #1
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