000			03632cam a2200373 i 4500
999			_c19305 _d19305
001			17442675
003			OSt
005			20230131163959.0
008			120827s2013 flua b 001 0 eng
010			_a 2012026280
020			_a9781584888499 (paperback : acidfree paper)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
082	0	0	_a519.542 LUN _223
100	1		_aLunn, David, _d1971-
245	1	4	_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, _c[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."
504			_aIncludes bibliographical references (pages 357-371) and index.
505			_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 Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices. Bibliography. Index.
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.
630	0	0	_aBUGS.
650		0	_aBayesian statistical decision theory.
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
856	4	2	_3Cover image _uhttp://jacketsearch.tandf.co.uk/common/jackets/covers/websmall/978158488/9781584888499.jpg
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cRFT