Information Theory, Inference and Learning Algorithms

Name: Information Theory, Inference and Learning Algorithms
Availability: InStock
ISBN: 9780521642989

ISBN-10: 0521642981

ISBN-13: 9780521642989

Edition: 2003

Authors: David J. C. MacKay

List price: $69.99

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Description:

Information theory, probability, coding and computation lie at the heart of some of the most dynamic areas of contemporary science and engineering. This text introduces mathematical technology in tandem with applications, providing motivation and hands-on guidance for problem-solving and modelling.

Book details

List price: $69.99
Copyright year: 2003
Publisher: Cambridge University Press
Publication date: 9/25/2003
Binding: Hardcover
Pages: 640
Size: 7.68" wide x 10.00" long x 1.34" tall
Weight: 3.608
Language: English




Introduction to information theory



Probability, entropy and inference



More about inference



Data Compression



The source coding theorem



Symbol codes



Stream codes



Codes for integers



Noisy-Channel Coding



Dependent random variables



Communication over a noisy channel



The noisy-channel coding theorem



Error-correcting codes and real channels



Further Topics in Information Theory



Hash codes



Binary codes



Very good linear codes exist



Further exercises on information theory



Message passing



Constrained noiseless channels



Crosswords and codebreaking



Why have sex? Information acquisition and evolution



Probabilities and Inference



An example inference task: clustering



Exact inference by complete enumeration



Maximum likelihood and clustering



Useful probability distributions



Exact marginalization



Exact marginalization in trellises



Exact marginalization in graphs



Laplace's method



Model comparison and Occam's razor



Monte Carlo methods



Efficient Monte Carlo methods



Ising models



Exact Monte Carlo sampling



Variational methods



Independent component analysis



Random inference topics



Decision theory



Bayesian inference and sampling theory



Neural Networks



Introduction to neural networks



The single neuron as a classifier



Capacity of a single neuron



Learning as inference



Hopfield networks



Boltzmann machines



Supervised learning in multilayer networks



Gaussian processes



Deconvolution



Sparse Graph Codes



Low-density parity-check codes



Convolutional codes and turbo codes



Repeat-accumulate codes



Digital fountain codes






Notation



Some physics



Some mathematics


Bibliography


Index