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Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. 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…mehr

Produktbeschreibung
Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. 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.


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  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Seitenzahl: 268
  • Erscheinungstermin: 07.07.2007
  • Englisch
  • ISBN-13: 9780387713854
  • Artikelnr.: 43729668
Inhaltsangabe
An 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.