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Bayesian Computation with R

  • Textbook
  • © 2009

Overview

Authors:
  1. Jim Albert
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  • Introduces Bayesian modeling by use of computation using the R language
  • Includes supplementary material: sn.pub/extras

Part of the book series: Use R! (USE R)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. An Introduction to R

    • Jim Albert
    Pages 1-17

  3. Introduction to Bayesian Thinking

    • Jim Albert
    Pages 19-37

  4. Single-Parameter Models

    • Jim Albert
    Pages 39-61

  5. Multiparameter Models

    • Jim Albert
    Pages 63-86

  6. Introduction to Bayesian Computation

    • Jim Albert
    Pages 87-115

  7. Markov Chain Monte Carlo Methods

    • Jim Albert
    Pages 117-152

  8. Hierarchical Modeling

    • Jim Albert
    Pages 153-179

  9. Model Comparison

    • Jim Albert
    Pages 181-204

  10. Regression Models

    • Jim Albert
    Pages 205-234

  11. Gibbs Sampling

    • Jim Albert
    Pages 235-264

  12. Using R to Interface with WinBUGS

    • Jim Albert
    Pages 265-286

  13. Back Matter

    Pages 1-12
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Keywords

About this book

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).

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