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.Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each ofthe models. 2007. 270 pp. ( Springer Texts in Statistics) Hardcover ISBN 978-0-387-38979-0 Pattern Recognition and Machine Learning Christopher M. Bishop 1a#39; If- .; a#39; Faquot; I. aquot; 3a#39; PAa#39; ITERN ... No previous knowledge of pattern recognition or machine learning concepts is assumed.
|Title||:||Bayesian Computation with R|
|Publisher||:||Springer Science & Business Media - 2007-07-07|