## Bayesian Methods for Data Analysis, Third EditionBroadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students. |

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### Contents

1 | |

CHAPTER 2 The Bayes approach | 15 |

CHAPTER 3 Bayesian computation | 105 |

CHAPTER 4 Model criticism and selection | 167 |

CHAPTER 5 The empirical Bayes approach | 225 |

CHAPTER 6 Bayesian design | 269 |

CHAPTER 7 Special methods and models | 311 |

CHAPTER 8 Case studies | 373 |

APPENDIX A Distributional catalog | 419 |

APPENDIX B Decision theory | 429 |

APPENDIX C Answers to selected exercises | 445 |

487 | |

Back cover | 521 |

### Other editions - View all

Bayes and Empirical Bayes Methods for Data Analysis, Second Edition Bradley P. Carlin,Thomas A. Louis No preview available - 2010 |

### Common terms and phrases

approximation assume baseline Bayes factor Bayes rule Bayesian approach Bayesian model beta BUGS code Carlin chain closed form components compute conditional distributions conjugate prior consider convergence covariate credible interval dataset empirical Bayes equation error evaluation example Figure frequentist full conditional distributions gamma Gaussian Gelfand Gibbs sampler given histogram hyperprior indifference zone interval iteration Jeffreys prior joint posterior likelihood loss function LVAD1 LVAD2 marginal distribution marginal likelihood marginal posterior matrix MCMC median methods Metropolis-Hastings algorithm Monte Carlo multivariate normal NPML observed data obtain optimal p-value parameter space patients percentiles plot point estimate posterior density posterior distribution posterior mean posterior probability prior distribution prior mean probability problem produce random effects regression risk sample sigma simulation specification statistical Subsection Suppose Table tion treatment univariate values variable variance vector WinBUGS