000			03975nam a22001937a 4500
003			OSt
005			20240827151604.0
008			191128b ||||| |||| 00| 0 eng d
020			_a9780521670517
040			_cTata Book House _aICTS-TIFR
050			_aQ360
100			_aMacKay, David J. C.
245			_aInformation theory, inference, and learning algorithms
260			_aNew Delhi: _bCambridge Uni. Press, _c[c2019]
300			_a628 p
505			_a1. 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
520			_a 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.
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