Information theory, inference, and learning algorithms
"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."--Jacket
Print Book, English, 2003
Cambridge University Press, Cambridge, UK, 2003
xii, 628 pages : illustrations ; 26 cm
9780521642989, 9780521644440, 0521642981, 0521644445
52377690
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. Correlated 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: codes for efficient information retrieval
13. Binary codes
14. Very good linear codes exist
15. Further exercises on information theory
16. Message passing
17. Communication over constrained noiseless channels
18. An aside: 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 and latent variable modelling
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