Bayesian reasoning and machine learning (Record no. 56469)

MARC details
000 -LEADER
fixed length control field 02205nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200306101035.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200306b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107439955
028 ## - DISTRIBUTOR NAME
Distributor Name Brijwasi Book Distributors
Distributor address H-87, Lalita Park,Laxmi Nagar,Delhi-110092
Bill Number BW/005744
Bill Date 28/02/2020
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 BAR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name David Barber
245 ## - TITLE STATEMENT
Title Bayesian reasoning and machine learning
Statement of responsibility, etc /David Barber
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Cambridge ; New York
Name of publisher, distributor, etc : Cambridge University Press
Date of publication, distribution, etc , 2012.
300 ## - PHYSICAL DESCRIPTION
Extent xxiv, 697 pages
Dimensions ; 26 cm
365 ## - TRADE PRICE
Price amount 1495
505 ## - FORMATTED CONTENTS NOTE
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 ## - SUMMARY, ETC.
Summary, etc 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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science
655 ## - INDEX TERM--GENRE/FORM
Genre/form data or focus term Machine learning
Non-focus term Bayesian statistical decision theory
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name David Barber
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Checked out Date last seen Date checked out Price effective from Koha item type
    Dewey Decimal Classification     SNU LIBRARY SNU LIBRARY 06/03/2020 Brijwasi Book Distributors 1495.00 2 2 006.31 BAR 27430 03/10/2022 04/04/2022 04/04/2022 06/03/2020 Books
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