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Information Theory, Inference, and Learning Algorithms

4.53 • 501 ratings • 36 reviews
Overview
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.
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Book details & editions

ISBN 0521642981
Publisher
Publication date June 2002
Language English
Pages 271 pages
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About the Author
David J.C. MacKay

David J.C. MacKay

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Known for writing in a clear, engaging, and easy-to-follow style. The work feels natural and flows smoothly, making it enjoyable from beginning to end.

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