Bayesian Computation with R

1. Front Matter

   Pages i-x

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2. An Introduction to R

   Pages 1-17

3. Introduction to Bayesian Thinking

   Pages 19-37

4. Single-Parameter Models

   Pages 39-56

5. Multiparameter Models

   Pages 57-74

6. Introduction to Bayesian Computation

   Pages 75-99

7. Markov Chain Monte Carlo Methods

   Pages 101-135

8. Hierarchical Modeling

   Pages 137-161

9. Model Comparison

   Pages 163-185

10. Regression Models

    Pages 187-210

11. Gibbs Sampling

    Pages 211-236

12. Using R to Interface with WinBUGS

    Pages 237-258

13. Back Matter

    Pages 259-267

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There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can • write short scripts to de?ne a Bayesian model • use or write functions to summarize a posterior distribution • use functions to simulate from the posterior distribution • construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

• Bayesian Inference
• Hierarchical modeling
• Markov chain Monte Carlo
• Monte Carlo
• Monte Carlo method
• Regression
• STATISTICA
• WinBUGS
• algorithms
• linear regression
• model
• modeling
• simulation

From the reviews:

The book is a concise presentation of a wide range of Bayesian inferential problems and the computational methods to solve them. The detailed and thorough presentation style, with complete R code for the examples, makes it a welcome companion to a theoretical text on Bayesian inference.... Smart students of statistics will want to have both R and Bayesian inference in their portfolio. Jim Albert's book is a good place to try out R while learning various computational methods for Bayesian inference. (Jouni Kerman, Teh American Statistician, February 2009, Vol. 63, No.1)

"This is a compact text, with 11 chapters. Overall it is well written and contains a plethora of interesting examples … . Each chapter ends with short notes on further reading, a summary of R commands that are introduced, and a collection of excellent exercises to test understanding of the material. … this book would be a useful companion to an introductory Bayesian text in a classroom setting or as a primer on R for a Bayesian practitioner." (John Verzani, SIAM reviews, Vol. 50 (4), December, 2008)

"This textbook is a compact introduction to modern computational Bayesian statistics. Without caring too much about mathematical details, the author gives an overall view of the main problems in statistics … . The examples and the applications provided are intended for a general audience of students." (Mauro Gasparini, Zentralblatt MATH, Vol. 1160, 2009)

“A book about Bayesian computation is highly welcome. … The book contains many interesting examples and is especially stimulating for students who start writing their own Bayesian programs. … This book serves this demand of students perfectly. … Thus, the book can be highly recommended for all introductory Bayes courses, preferably if the students had a statistics course with an introduction to R (or Splus) before.”­­­ (Wolfgang Polasek, Statistical Papers, Vol. 52,2011)

• Bowling Green State University, USA

  Jim Albert

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