Information Theory, Inference and Learning Algorithms MacKay Hardback

Description: Information Theory, Inference and Learning Algorithms Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering. David J. C. MacKay (Author) 9780521642989, Cambridge University Press Hardback, published 25 September 2003 640 pages 25.4 x 19.5 x 3.4 cm, 1.525 kg 'With its breadth, accessibility and handsome design, this book should prove to be quite popular. Highly recommended as a primer for students with no background in coding theory, the set of chapters on error correcting codes are an excellent brief introduction to the elements of modern sparse graph codes: LDPC, turbo, repeat-accumulate and fountain codes are described clearly and succinctly.' IEEE Transactions on Information Theory 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. 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. Subject Areas: Computer science [UY], Electronics & communications engineering [TJ], Physics [PH]

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