Bayesian Computation with R (Use R) - Info and Reading Options
By Jim Albert
"Bayesian Computation with R (Use R)" is published by Springer in July 31, 2007, it has 270 pages and the language of the book is English.
“Bayesian Computation with R (Use R)” Metadata:
- Title: ➤ Bayesian Computation with R (Use R)
- Author: Jim Albert
- Language: English
- Number of Pages: 270
- Publisher: Springer
- Publish Date: July 31, 2007
“Bayesian Computation with R (Use R)” Subjects and Themes:
- 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
Edition Identifiers:
- The Open Library ID: OL7447850M - OL3233820W
- Online Computer Library Center (OCLC) ID: 124958652 - 779892135
- Library of Congress Control Number (LCCN): 2007929182
- ISBN-13: 9780387713847
- ISBN-10: 0387713840
- All ISBNs: 0387713840 - 9780387713847
AI-generated Review of “Bayesian Computation with R (Use R)”:
"Bayesian Computation with R (Use R)" Description:
The Open Library:
"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.
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