MARC Record from harvard_bibliographic_metadata

LEADER: 03626cam a2200421 i 4500
001 013673318-2
005 20130522105200.0
008 120827s2013 flua b 001 0 eng
010 $a 2012026280
020 $a9781584888499 (paperback : acid-free paper)
035 0 $aocn808810636
040 $aDLC$beng$cDLC$erda$dDLC
042 $apcc
050 00 $aQA279.5$b.L86 2013
082 00 $a519.5/42$223
084 $aMAT029000$2bisacsh
100 1 $aLunn, David,$d1971-
245 14 $aThe BUGS book :$ba practical introduction to Bayesian analysis /$cDavid Lunn, Christopher Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter.
264 1 $aBoca Raton, FL :$bCRC Press, Taylor & Francis Group,$c2013, ©2013.
300 $axvii, 381 pages :$billustrations ;$c24 cm.
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 0 $aTexts in statistical science
500 $a" A Chapman & Hall book."
520 $a"Preface. History. Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown quantities are simulated from their appropriate probability distribution, have revolutionised the practice of statistics. For more than 20 years the BUGS project has been at the forefront of this movement. The BUGS project began in Cambridge, in 1989, just as Alan Gelfand and Adrian Smith were working 80 miles away in Nottingham on their classic Gibbs sampler paper (Gelfand and Smith, 1990) that kicked off the revolution. But we never communicated (except through the intermediate node of David Clayton) and whereas the Gelfand-Smith approach used image-processing as inspiration, the philosophy behind BUGS was rooted more in techniques for handling uncertainty in artificial intelligence using directed graphical models and what came to be called Bayesian networks (Pearl, 1988). Lunn et al. (2009b) lay out all this history in greater detail. Some people have accused Markov chain Monte Carlo methods of being slow, but nothing could compare with the time it has taken this book to be written! The first proposal dates from 1995, but things got in the way, as they do, and it needed a vigorous new generation of researchers to finally get it finished. It is slightly galling that much of the current book could have been written in the mid-1990s, since the basic ideas of the software, the language for model description, and indeed some of the examples are unchanged. Nevertheless there have been important developments in the extended gestational period of the book, for example techniques for model criticism and comparison, implementation of differential equations and nonparametric techniques, and the ability to run BUGS code within a range of alternative programs"--$cProvided by publisher.
504 $aIncludes bibliographical references (pages 357-371) and index.
505 0 $aIntroduction : probability and parameters -- Monte Carlo simulations using BUGS -- Introduction to Bayesian inference -- Introduction to Markov chain Monte Carlo methods -- Prior distributions -- Regression models -- Categorical data -- Model checking and comparison -- Issues in modelling -- Hierarchical models -- Specialised models -- Different implementations of BUGS.
650 0 $aBayesian statistical decision theory.
630 00 $aBUGS.
650 7 $aMATHEMATICS / Probability & Statistics / General.$2bisacsh
700 1 $aJackson, Christopher,$eauthor.
700 1 $aBest, Nicky,$eauthor.
700 1 $aThomas, Andrew,$eauthor.
700 1 $aSpiegelhalter, D. J.,$eauthor.
899 $a415_565874
988 $a20130502
906 $0DLC