Information theory, inference, and learning algorithms
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書誌事項
Information theory, inference, and learning algorithms
Cambridge University Press, 2003
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注記
Includes bibliographical references (p. 613-619) and index
Reprinted with corrections 2004: a small number of typographical errors were corrected, and the design of the book was altered slightly. Page-numbering generally remains unchanged except in chapters 1, 6, and 28, where a few paragraphs, figures, and equations have moved around. All equations, sections, and exercise numbers are unchanged.
内容説明・目次
内容説明
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.
目次
- 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
- Bibliography
- Index.
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