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Bayesian Time Series Models / (Record no. 37816)

MARC details
000 -LEADER
fixed length control field 02289nam a22003377a 4500
001 - CONTROL NUMBER
control field sulb-eb0016378
003 - CONTROL NUMBER IDENTIFIER
control field BD-SySUS
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20160405135335.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 101018s2011||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780511984679 (ebook)
Canceled/invalid ISBN 9780521196765 (hardback)
040 ## - CATALOGING SOURCE
Original cataloging agency UkCbUP
Language of cataloging eng
Description conventions rda
Transcribing agency UkCbUP
Modifying agency BD-SySUS
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA280
Item number .B39 2011
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5/5
Edition number 22
245 00 - TITLE STATEMENT
Title Bayesian Time Series Models /
Statement of responsibility, etc. edited by David Barber, A. Taylan Cemgil, Silvia Chiappa.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge :
Name of producer, publisher, distributor, manufacturer Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2011.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (432 pages) :
Other physical details digital, PDF file(s).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
500 ## - GENERAL NOTE
General note Title from publisher's bibliographic system (viewed on 04 Apr 2016).
520 ## - SUMMARY, ETC.
Summary, etc. 'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Time-series analysis
Topical term or geographic name as entry element Bayesian statistical decision theory
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Barber, David,
Relator term editor.
Personal name Cemgil, A. Taylan,
Relator term editor.
Personal name Chiappa, Silvia,
Relator term editor.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9780521196765
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1017/CBO9780511984679">http://dx.doi.org/10.1017/CBO9780511984679</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type

No items available.