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Information Theory, Inference And Learning Algorithms

Information Theory, Inference And Learning Algorithms - Mackay, David J. C. (university Of Cambridge) - ISBN: 9780521642989
Prijs: € 69,20
Levertijd: 8 tot 12 werkdagen
Bindwijze: Boek, Gebonden
Genre: Natuurkunde algemeen
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Beschrijving

David MacKay breaks new ground in this entertaining textbook by giving a united introduction to information theory and inference. These topics underpin some of the most exciting areas of contemporary science and engineering. Theory is presented in tandem with applications and over 400 exercises, some with full solutions are provided. Ideal for courses in information, communication and coding for a new generation of students, this is an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, data mining, and financial engineering.

Details

Titel: Information Theory, Inference And Learning Algorithms
auteur: Mackay, David J. C. (university Of Cambridge)
Mediatype: Boek
Bindwijze: Gebonden
Taal: Engels
Aantal pagina's: 640
Uitgever: Cambridge University Press
Plaats van publicatie: 03
NUR: Natuurkunde algemeen
Afmetingen: 251 x 192 x 33
Gewicht: 1572 gr
ISBN/ISBN13: 0521642981
ISBN/ISBN13: 9780521642989
Intern nummer: 5960097

Quote

'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

Inhoudsopgave

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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