You cannot do inference without making assumptions.
An edition of Information Theory, Inference & Learning Algorithms
(2003)
Information Theory, Inference & Learning Algorithms
1st edition
by David J.C. MacKay
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Book Jacket:
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.
Publisher Description:
This textbook offers comprehensive coverage of Shannon's theory of information as well as the theory of neural networks and probabilistic data modelling. It includes explanations of Shannon's important source encoding theorem and noisy channel theorem as well as descriptions of practical data compression systems. Many examples and exercises make the book ideal for students to use as a class textbook, or as a resource for researchers who need to work with neural networks or state-of-the-art error-correcting codes.
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Previews available in: Undetermined English
Subjects
Information theory, Inference, Machine Learning, Bayesian, Aprendizado computacional, Information, Théorie de l', Inferenz, Statistische analyse, Toepassingen, Maschinelles Lernen, Informationstheorie, Teoria da informação, Informatietheorie, Algoritmen, Algorithms, Teoria da informacao, Information, Theorie de l', Inferenz <künstliche intelligenz>, Inferenz (künstliche intelligenz), Q360 .m23 2003, 003/.54, Dat 708f, Qh 210, Sk 880, St 130, St 300| Edition | Availability |
|---|---|
1
Information Theory, Inference and Learning Algorithms
2004, University of Cambridge ESOL Examinations, TBS
in English
0521644445 9780521644440
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2
INFORMATION THEORY, INFERENCE, AND LEARNING ALGORITHMS.
2003, CAMBRIDGE UNIV PRESS, Cambridge University Press
in Undetermined
0521642981 9780521642989
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3
Information Theory, Inference & Learning Algorithms
2003, Cambridge University Press
Hardcover
in English
- 1st edition
0521642981 9780521642989
|
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Book Details
First Sentence
"In this chapter we discuss how to measure the information content of the outcome of a random experiment."
Table of Contents
Preface.
Edition Notes
Full text is online.
Classifications
The Physical Object
ID Numbers
Excerpts
Page 26,
added by David.
A central theme of the book.
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| September 4, 2024 | Edited by MARC Bot | import existing book |
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