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Bayesian reasoning and machine learn...

Barber, David, (1968-)

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Bayesian reasoning and machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian reasoning and machine learning // David Barber.
Author:	Barber, David,
Published:	Cambridge ;Cambridge University Press, : 2012.,
Description:	xxiv, 697 p. :ill. ;26 cm.
[NT 15003449]:	Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.
Subject:	Machine learning. -
Online resource:	http://assets.cambridge.org/97805215/18147/cover/9780521518147.jpg
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011035553-b.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html
ISBN:	9780521518147

Bayesian reasoning and machine learning /
Barber, David,1968-

Bayesian reasoning and machine learning /David Barber. - Cambridge ;Cambridge University Press,2012. - xxiv, 697 p. :ill. ;26 cm.

Includes bibliographical references and index.

Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.

"Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online"--

ISBN: 9780521518147

LCCN: 2011035553Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA267 / .B347 2012

Dewey Class. No.: 006.3/1

Bayesian reasoning and machine learning /
LDR:04221cam 2200277 a 4500 001 2235206
005 20160525120211.0
008 211109s2012 enka b 001 0 eng
010 $a 2011035553
020 $a 9780521518147
035 $a 16931139
040 $a DLC $c DLC $d DLC
042 $a pcc
050 0 0 $a QA267 $b .B347 2012
082 0 0 $a 006.3/1 $2 23
084 $a COM016000 $2 bisacsh
100 1 $a Barber, David, $d 1968- $3 3485099
245 1 0 $a Bayesian reasoning and machine learning / $c David Barber.
260 # $a Cambridge ; $a New York : $b Cambridge University Press, $c 2012.
300 $a xxiv, 697 p. : $b ill. ; $c 26 cm.
504 $a Includes bibliographical references and index.
505 8 # $a Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.
520 # $a "Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online"-- $c Provided by publisher.
520 # $a "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"-- $c Provided by publisher.
650 # 0 $a Machine learning. $3 533906
650 # 0 $a Bayesian statistical decision theory. $3 551404
650 # 7 $a COMPUTERS / Computer Vision & Pattern Recognition. $2 bisacsh $3 3485100
856 4 2 $3 Cover image $u http://assets.cambridge.org/97805215/18147/cover/9780521518147.jpg
856 4 2 $3 Contributor biographical information $u http://www.loc.gov/catdir/enhancements/fy1117/2011035553-b.html
856 4 2 $3 Publisher description $u http://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html
856 4 1 $3 Table of contents only $u http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html