000			04055nam a22005055i 4500
001			978-0-387-71385-4
003			DE-He213
005			20250710084012.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387713854 _a99780387713854
024	7		_a10.1007/978-0-387-71385-4 _2doi
082	0	4	_a519.5 _223
100	1		_aAlbert, Jim. _eeditor.
245	1	0	_aBayesian Computation with R _h[recurso electrónico] / _cedited by Jim Albert.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUse R!
505	0		_aAn Introduction to R -- to Bayesian Thinking -- Single-Parameter Models -- Multiparameter Models -- to Bayesian Computation -- Markov Chain Monte Carlo Methods -- Hierarchical Modeling -- Model Comparison -- Regression Models -- Gibbs Sampling -- Using R to Interface with WinBUGS.
520			_aThere has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab.
650		0	_aSTATISTICS.
650		0	_aCOMPUTER SIMULATION.
650		0	_aCOMPUTER SCIENCE _xMATHEMATICS.
650		0	_aVISUALIZATION.
650		0	_aMATHEMATICAL OPTIMIZATION.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.
650	2	4	_aSIMULATION AND MODELING.
650	2	4	_aCOMPUTATIONAL MATHEMATICS AND NUMERICAL ANALYSIS.
650	2	4	_aVISUALIZATION.
650	2	4	_aOPTIMIZATION.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387713847
830		0	_aUse R!
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-71385-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58242 _d58242