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Information theory, inference, and l...

MacKay, David J. C.

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Information theory, inference, and learning algorithms /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Information theory, inference, and learning algorithms // David J.C. MacKay.
Author:	MacKay, David J. C.
Published:	Cambridge, UK ;Cambridge University Press, : 2003.,
Description:	xii, 628 p. :ill. ;26 cm.
[NT 15003449]:	Data compression -- Noisy-channel coding -- Further topics in information theory -- Probabilities and inference -- Neural networks -- Sparse graph codes.
Subject:	Information theory. -
ISBN:	9780521642989

Information theory, inference, and learning algorithms /
MacKay, David J. C.

Information theory, inference, and learning algorithms /David J.C. MacKay. - Cambridge, UK ;Cambridge University Press,2003. - xii, 628 p. :ill. ;26 cm.

Includes bibliographical references (p. 613-619) and index.

Data compression -- Noisy-channel coding -- Further topics in information theory -- Probabilities and inference -- Neural networks -- Sparse graph codes.

"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."--BOOK JACKET.

ISBN: 9780521642989UK57.00

LCCN: 2003055133Subjects--Topical Terms:

542527
Information theory.

LC Class. No.: Q360 / .M23 2003

Dewey Class. No.: 003/.54

Information theory, inference, and learning algorithms /
LDR:01397cam a22002174a 4500 001 2383463
003 OCoLC
005 20120423233025.0
008 250605s2003 enka b 001 0 eng
010 $a 2003055133
020 $a 9780521642989 $q (hbk.) : $c UK57.00
020 $a 0521642981 $q (hbk.)
040 $a DLC $b eng $c DLC $d C#P $d UKM $d YBM $d BAKER $d NLGGC $d UBA $d BTCTA $d LVB $d YDXCP $d IG# $d HEBIS $d DEBBG $d OCLCQ $d MNE $d NDHU
042 $a pcc
050 0 0 $a Q360 $b .M23 2003
082 0 0 $a 003/.54 $2 21
100 1 $a MacKay, David J. C. $3 3756727
245 1 0 $a Information theory, inference, and learning algorithms / $c David J.C. MacKay.
260 # $a Cambridge, UK ; $a New York : $b Cambridge University Press, $c 2003.
300 $a xii, 628 p. : $b ill. ; $c 26 cm.
504 $a Includes bibliographical references (p. 613-619) and index.
505 0 # $a Data compression -- Noisy-channel coding -- Further topics in information theory -- Probabilities and inference -- Neural networks -- Sparse graph codes.
520 1 $a "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."--BOOK JACKET.
650 # 0 $a Information theory. $3 542527