## Information Theory, Inference and Learning AlgorithmsInformation 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. |

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### Contents

Introduction to Information Theory | 3 |

Probability Entropy and Inference | 22 |

ful theoretical ideas of Shannon but also practical solutions to communica | 34 |

More about Inference | 48 |

Data Compression | 65 |

The Source Coding Theorem | 67 |

Symbol Codes | 91 |

Stream Codes | 110 |

Stream Codes 26 I Exact Margmalization in Graphs | 346 |

Monte Carlo Methods | 357 |

Efficient Monte Carlo Methods | 387 |

Ising Models | 400 |

Exact Monte Carlo Sampling | 413 |

Variational Methods | 422 |

Independent Component Analysis and Latent Variable Mod elling | 437 |

Random Inference Topics | 445 |

Codes for Integers | 132 |

NoisyChannel Coding | 137 |

Dependent Random Variables | 138 |

Communication over a Noisy Channel | 146 |

The NoisyChannel Coding Theorem | 162 |

ErrorCorrecting Codes and Real Channels | 177 |

Further Topics in Information Theory | 191 |

Codes for Efficient Information Retrieval | 193 |

Binary Codes | 206 |

Very Good Linear Codes Exist | 229 |

Further Exercises on Information Theory | 233 |

Message Passing | 241 |

Communication over Constrained Noiseless Channels | 248 |

Crosswords and Codebreaking | 260 |

Why have Sex? Information Acquisition and Evolution | 269 |

Probabilities and Inference | 281 |

Clustering | 284 |

Exact Inference by Complete Enumeration | 293 |

Maximum Likelihood and Clustering | 300 |

Useful Probability Distributions | 311 |

Exact Marginalization | 319 |

Exact Marginalization in Trellises | 324 |

Exact Marginalization in Graphs | 334 |

Laplaces Method | 341 |

Model Comparison and Occams Razor | 343 |

Decision Theory | 451 |

Bayesian Inference and Sampling Theory | 457 |

Neural networks | 467 |

Introduction to Neural Networks | 468 |

The Single Neuron as a Classifier | 471 |

Capacity of a Single Neuron | 483 |

Learning as Inference | 492 |

Hopfield Networks | 505 |

Boltzmami Machines | 522 |

Supervised Learning in Multilayer Networks | 527 |

Gaussian Processes | 535 |

Deconvolution | 549 |

Sparse Graph Codes | 555 |

LowDensity ParityCheck Codes | 557 |

Convolutional Codes and Turbo Codes | 574 |

Repeat Accumulate Codes | 582 |

50 Digital Fountain Codes | 588 |

Digital Fountain Codes | 589 |

Appendices | 597 |

A Notation | 598 |

B Some Physics | 601 |

Some Mathematics | 605 |

613 | |

620 | |

### Common terms and phrases

approximation arithmetic coding assume average Bayesian binary symmetric channel blocklength capacity Chapter cluster codeword coding theorem compression compute decoding problem defined distance encoding energy ensemble entropy equal equation error probability estimate evaluate example fitness flipped Gaussian channel Gaussian distribution Gibbs sampling given graph Hamming code hash function Huffman code hypothesis independent inference input distribution integer interval Ising model iterations K-means algorithm linear code low-density parity-check codes marginal Markov chain maximal mean Metropolis method Monte Carlo methods mutual information neuron node noise level noisy channel normalizing constant obtain optimal outcome output parameters parity parity-check matrix posterior distribution posterior probability predictions prefix code prior probability density probability distribution probability of error random variable ratio sequence shown in figure shows simulation Solution to exercise source bits space standard deviation string symbol code transmitted trellis typical set uniquely decodeable variance vector weight zero