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|a 9780521642989 (rel.)
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|a 9780521642989
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|a eng
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|a 003.54
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|a 006.31
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|a E.4
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|a F.2
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|a H.1.1
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|a I.2.6
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|a 94-01. 2000
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|a 68P30. 2000
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|a 68T05. 2000
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|a 92C20. 2000
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|a 94A15. 2000
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| 100 |
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|a MacKay, David J. C.,
|d 1967-2016.
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| 245 |
1 |
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|a Information theory, inference, and learning algorithms
|c David J. C. MacKay.
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| 260 |
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|a Cambridge ;
|a New York :
|b Cambridge University Press.
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| 260 |
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|c C 2003.
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| 300 |
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|a 1 volume (xii-628 pages) :
|b illustrations, figures, couverture illustrée en couleurs ;
|c 26 cm.
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| 504 |
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|a Bibliographie p. 613-619. Index
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|a 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.
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| 650 |
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|a Information, Théorie de l'
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| 650 |
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|a Apprentissage automatique
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| 650 |
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|a Algorithmes
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| 650 |
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|a Inférence
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| 997 |
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|0 602788
|1 Livre papier
|a Ressource papier
|b INSA
|c 0/Blois/
|c 1/Blois/INSA CVL/
|z Blois, INSA CVL, 518.1 MAC
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