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Information Theory, Inference and Learning Algorithms > 패턴

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Information Theory, Inference and Learning Algorithms
판매가격 79,000원
저자 MacKay
도서종류 외국도서
출판사 Cambridge University Press
발행언어 영어
발행일 2003-10
페이지수 640
ISBN 9780521642989
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  • 도서 정보

    도서 상세설명

    Preface
    1 Introduction to Information Theory 3
    2 Probability, Entropy, and Inference 22
    3 More about Inference 48
    I Data Compression 65
    4 The Source Coding Theorem 67
    5 Symbol Codes 91
    6 Stream Codes 110
    7 Codes for Integers 132
    II Noisy-Channel Coding 137
    8 Correlated Random Variables 138
    9 Communication over a Noisy Channel 146
    10 The Noisy-Channel Coding Theorem 162
    11 Error-Correcting Codes and Real Channels 177
    III Further Topics in Information Theory 191
    12 Hash Codes: Codes for Efficient Information Retrieval 193
    13 Binary Codes 206
    14 Very Good Linear Codes Exist 229
    15 Further Exercises on Information Theory 233
    16 Message Passing 241
    17 Communication over Constrained Noiseless Channels 248
    18 Crosswords and Codebreaking 260
    19 Why have Sex? Information Acquisition and Evolution 269
    IV Probabilities and Inference 281
    20 An Example Inference Task: Clustering 284
    21 Exact Inference by Complete Enumeration 293
    22 Maximum Likelihood and Clustering 300
    23 Useful Probability Distributions 311
    24 Exact Marginalization 319
    25 Exact Marginalization in Trellises 324
    26 Exact Marginalization in Graphs 334
    27 Laplace\'s Method 341
    28 Model Comparison and Occam\'s Razor 343
    29 Monte Carlo Methods 357
    30 Efficient Monte Carlo Methods 387
    31 Ising Models 400
    32 Exact Monte Carlo Sampling 413
    33 Variational Methods 422
    34 Independent Component Analysis and Latent Variable Modelling 437
    35 Random Inference Topics 445
    36 Decision Theory 451
    37 Bayesian Inference and Sampling Theory 457
    V Neural networks 467
    38 Introduction to Neural Networks 468
    39 The Single Neuron as a Classifier 471
    40 Capacity of a Single Neuron 483
    41 Learning as Inference 492
    42 Hopfield Networks 505
    43 Boltzmann Machines 522
    44 Supervised Learning in Multilayer Networks 527
    45 Gaussian Processes 535
    46 Deconvolution 549
    VI Sparse Graph Codes 555
    47 Low-Density Parity-Check Codes 557
    48 Convolutional Codes and Turbo Codes 574
    49 Repeat-Accumulate Codes 582
    50 Digital Fountain Codes 589
    VII Appendices 597
    A: Notation 598
    B: Some Physics 601
    C: Some Mathematics 605
    Bibliography 613
    Index 620
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