Information Theory, Inference and Learning Algorithms (Record no. 115960)
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| 000 -LEADER | |
|---|---|
| fixed length control field | nam a22 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190411b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780521642989 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 003.54 |
| Item number | MAC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | MacKay, David J. C. |
| 9 (RLIN) | 52113 |
| 245 ## - TITLE STATEMENT | |
| Title | Information Theory, Inference and Learning Algorithms |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Cambridge University Press |
| Place of publication, distribution, etc | UK |
| Date of publication, distribution, etc | 2003 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 628p |
| 500 ## - GENERAL NOTE | |
| General note | Introduction to information theory<br/>Probability, entropy and inference<br/>More about inference<br/>Part - 1: Data Compression:<br/>The source coding theorem<br/>Symbol codes<br/>Stream codes<br/>Codes for integers<br/>Part - 2: Noisy-Channel Coding:<br/>Dependent random variables<br/>Communication over a noisy channel<br/>The noisy-channel coding theorem<br/>Error-correcting codes and real channels<br/>Part - 3: Further Topics in Information Theory:<br/>Hash codes<br/>Binary codes<br/>Very good linear codes exist<br/>Further exercises on information theory<br/>Message passing<br/>Constrained noiseless channels<br/>Crosswords and codebreaking<br/>Why have sex? Information acquisition and evolution<br/>Part - 4: Probabilities and Inference:<br/>An example inference task: clustering<br/>Exact inference by complete enumeration<br/>Maximum likelihood and clustering<br/>Useful probability distributions<br/>Exact marginalization<br/>Exact marginalization in trellises<br/>Exact marginalization in graphs<br/>Laplace's method<br/>Model comparison and Occam's razor<br/>Monte Carlo methods<br/>Efficient Monte Carlo methods<br/>Ising models<br/>Exact Monte Carlo sampling<br/>Variational methods<br/>Independent component analysis<br/>Random inference topics<br/>Decision theory<br/>Bayesian inference and sampling theory<br/>Part - 5: Neural Networks:<br/>Introduction to neural networks<br/>The single neuron as a classifier<br/>Capacity of a single neuron<br/>Learning as inference<br/>Hopfield networks<br/>Boltzmann machines<br/>Supervised learning in multilayer networks<br/>Gaussian processes<br/>Deconvolution<br/>Part - 6: Sparse Graph Codes<br/>Low-density parity-check codes<br/>Convolutional codes and turbo codes<br/>Repeat-accumulate codes<br/>Digital fountain codes<br/>Part - 7: Appendices: A. <br/>Some physics<br/>Some mathematics |
| 600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME | |
| Personal name | Computer Engineering |
| 9 (RLIN) | 37072 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Item type | Book |
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