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Information Theory, Inference, and Learning Algorithms

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 N/A
Publication date June 2002
Language English
Pages pages
Reading Options PDF · EPUB · Mobi
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About the Author
David J.C. MacKay

David J.C. MacKay

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David J.C. MacKay is 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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