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[DOWNLOAD^^][PDF] Information Theory, Inference and Learning Algorithms [PDF,
mobi, ePub]

[DOWNLOAD^^][PDF] Information Theory, Inference and Learning Algorithms [PDF, mobi, ePub]
[DOWNLOAD^^][PDF]
Information Theory,
Inference and
Learning Algorithms
[PDF, mobi, ePub]
Description
'...a valuable reference...enjoyable and highly useful.' American Scientist'...an impressive book,
intended as a class text on the subject of the title but having the character and robustness of a
focused encyclopedia. The presentation is finely detailed, well documented, and stocked with
artistic flourishes.' Mathematical Reviews'Essential reading for students of electrical engineering
and computer science; also a great heads-up for mathematics students concerning the subtlety of
many commonsense questions.' Choice'An utterly original book that shows the connections
between such disparate fields as information theory and coding, inference, and statistical physics.'
Dave Forney, Massachusetts Institute of Technology'This is an extraordinary and important book,
generous with insight and rich with detail in statistics, information theory, and probabilistic
modeling across a wide swathe of standard, creatively original, and delightfully quirky topics. David
MacKay is an uncompromisingly lucid thinker, from whom students, faculty and practitioners all
can learn.' Peter Dayan and Zoubin Ghahramani, Gatsby Computational Neuroscience Unit,
University College, London'An instant classic, covering everything from Shannon's fundamental
theorems to the postmodern theory of LDPC codes. You'll want two copies of this astonishing
book, one for the office and one for the fireside at home.' Bob McEliece, California Institute of
Technology'An excellent textbook in the areas of infomation theory, Bayesian inference and
learning alorithms. Undergraduate and post-graduate students will find it extremely useful for
gaining insight into these topics.' REDNOVA'Most of the theories are accompanied by motivations,
and explanations with the corresponding examples...the book achieves its goal of being a good
textbook on information theory.' ACM SIGACT News Read more Information theory and inference,
often taught separately, are here united in one entertaining textbook. These topics lie at the heart
of many exciting areas of contemporary science and engineering - communication, signal
processing, data mining, machine learning, pattern recognition, computational neuroscience,
bioinformatics, and cryptography. 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. The final part of the book describes
the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes,

and digital fountain codes -- the twenty-first century standards for