Bayesian computation with R / Jim Albert
Tipo de material:
TextoSeries Detalles de publicación: New York : Springer, c2007.Descripción: x, 267 p. : il., gráficas. ; 24 cmISBN: - 9780387713847 (pbk.)
- 0387713840 (pbk.)
- QA279.5 A3331
| Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Signatura topográfica | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | |
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Libros para consulta en sala
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Biblioteca Antonio Enriquez Savignac | Biblioteca Antonio Enriquez Savignac | COLECCIÓN RESERVA | QA279.5 A3331 (Navegar estantería(Abre debajo)) | 1 | No para préstamo | Ing. Telematica | 018282 |
Incluye referencias bibliográficas p. [259]-262 e índice
An introduction to R -- Introduction to Bayesian thinking -- Single parameter models -- Multiparameter models -- Introduction to Bayesian computation -- Markov chain Monte Carlo methods -- Hierarchical modeling -- Model comparison -- Regression models -- Gibbs sampling -- Using R to interface with WinBUGS
"There 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. Also the book 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."--P. web LC
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