Title: Bayesian Computation with R (Use R)
First published: July 31, 2007
Description: "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"--Jacket.
Subjects: Bayesian statistical decision theory, Data processing, R (Computer program language), Statistics, Bayes Theorem, Software, Methode van Bayes, R (computerprogramma), Computer simulation, Computer science, Mathematics, Visualization, Mathematical optimization, Mathematical statistics, Statistics and Computing/Statistics Programs, Simulation and Modeling, Computational Mathematics and Numerical Analysis, Optimization