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- Author: Jim Albert
- Publisher: Springer-Verlag GmbH
- Year: 2007
- ISBN: 9780387713854
- ISBN-10: 0387713859
- ISBN-13: 9780387713854
- Format: PDF
- Language: English
Reviews
Description
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.
- Author: Jim Albert
- Publisher: Springer-Verlag GmbH
- Year: 2007
- ISBN: 9780387713854
- ISBN-10: 0387713859
- ISBN-13: 9780387713854
- Format: PDF
- Language: English English
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.
Reviews