Information Theory, Inference & Learning Algorithms
Edition: 1st edition
Publisher: Cambridge University Press
Published: 2003
Place of publication: Cambridge, UK; New York, USA
Language: eng
Format: Hardcover
Number of pages: 640
ISBN-13: 9780521642989
Copyright date: 2003
Subjects: Applications of Computing; Computer Programming; Electronics & Communications Engineering; Theoretical methods; Information Theory; Algorithms (Computer Programming); Computers; Computers - General Information; Computer Books: General; Neural Networks; Data Modeling & Design; Computers / Application Software / General; Programming - General
First sentence: In this chapter we discuss how to measure the information content of the outcome of a random experiment.
Notes: Full text is online.
Table of contents: label: Preface
pagenum: v; label: Chapter 1
title: Introduction to information theory
pagenum: 3; label: Chapter 2
title: Probability, entropy, and inference
pagenum: 22; label: Chapter 3
title: More about inference
pagenum: 48; label: Part I
title: Data Compression
pagenum: 65; label: Chapter 4
title: The source coding theorem
pagenum: 67; label: Chapter 5
title: Symbol codes
pagenum: 91; label: Chapter 6
title: Stream codes
pagenum: 110; label: Chapter 7
title: Codes for integers
pagenum: 132; label: Part II
title: Noisy-Channel Coding
pagenum: 137; label: Chapter 8
title: Correlated random variables
pagenum: 138; label: Chapter 9
title: Communication over a noisy channel
pagenum: 146; label: Chapter 10
title: The noisy-channel coding theorem
pagenum: 162; label: Chapter 11
title: Error-correcting codes and real channels
pagenum: 177; label: Part III
title: Further Topics in Information Theory
pagenum: 191; label: Chapter 12
title: Hash codes: codes for efficient information retrieval
pagenum: 193; label: Chapter 13
title: Binary codes
pagenum: 206; label: Chapter 14
title: Very good linear codes exist
pagenum: 229; label: Chapter 15
title: Further exercises on information theory
pagenum: 233; label: Chapter 16
title: Message passing
pagenum: 241; label: Chapter 17
title: Communication over constrained noiseless channels
pagenum: 248; label: Chapter 18
title: An aside: crosswords and codebreaking
pagenum: 260; label: Chapter 19
title: Why have sex? Information acquisition and evolution
pagenum: 269; label: Part IV
title: Probabilities and Inference
pagenum: 281; label: Chapter 20
title: An example inference task: clustering
pagenum: 284; label: Chapter 21
title: Exact inference by complete enumeration
pagenum: 293; label: Chapter 22
title: Maximum likelihood and clustering
pagenum: 300; label: Chapter 23
title: Useful probability distributions
pagenum: 311; label: Chapter 24
title: Exact marginalization
pagenum: 319; label: Chapter 25
title: Exact marginalization in trellises
pagenum: 324; label: Chapter 26
title: Exact marginalization in graphs
pagenum: 334; label: Chapter 27
title: Laplace's method
pagenum: 341; label: Chapter 28
title: Model comparison and Occam's razor
pagenum: 343; label: Chapter 29
title: Monte Carlo methods
pagenum: 357; label: Chapter 30
title: Efficient Monte Carlo methods
pagenum: 387; label: Chapter 31
title: Ising models
pagenum: 400; label: Chapter 32
title: Exact Monte Carlo sampling
pagenum: 413; label: Chapter 33
title: Variational methods
pagenum: 422; label: Chapter 34
title: Independent component analysis and latent variable modelling
pagenum: 437; label: Chapter 35
title: Random inference topics
pagenum: 445; label: Chapter 36
title: Decision theory
pagenum: 451; label: Chapter 37
title: Bayesian inference and sampling theory
pagenum: 457; label: Part V
title: Neural Networks
pagenum: 467; label: Chapter 38
title: Introduction to neural networks
pagenum: 468; label: Chapter 39
title: The single neuron as a classifier
pagenum: 471; label: Chapter 40
title: Capacity of a single neuron
pagenum: 483; label: Chapter 41
title: Learning as inference
pagenum: 492; label: Chapter 42
title: Hopfield networks
pagenum: 505; label: Chapter 43
title: Boltzmann machines
pagenum: 522; label: Chapter 44
title: Supervised learning in multilayer networks
pagenum: 527; label: Chapter 45
title: Gaussian processes
pagenum: 535; label: Chapter 46
title: Deconvolution
pagenum: 549; label: Part VI
title: Sparse Graph Codes
pagenum: 555; label: Chapter 47
title: Low-density parity-check codes
pagenum: 557; label: Chapter 48
title: Convolutional codes and turbo codes
pagenum: 574; label: Chapter 49
title: Repeat-accumulate codes
pagenum: 582; label: Chapter 50
title: Digital fountain codes
pagenum: 589; label: Part VII
title: Appendices
pagenum: 597; label: Appendix A
title: Notation
pagenum: 598; label: Appendix B
title: Some physics
pagenum: 601; label: Appendix C
title: Some mathematics
pagenum: 605; label: Bibliography
pagenum: 613; label: Index
pagenum: 620