Bayesian Methods for Data AnalysisBroadening 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 ( |
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Bradley P. Carlin, Thomas A. Louis. Texts in Statistical Science Bayesian Methods for Data Analysis Third Edition mu lag 1 act = 0.834 025, 500, 975 duantiles are 1.78 1.81 1.83 . |al 025, 500. 975 guantiles are -4.34-3.97-361 - -T * 025 ...
Bradley P. Carlin, Thomas A. Louis. Texts in Statistical Science Bayesian Methods for Data Analysis Third Edition mu lag 1 act = 0.834 025, 500, 975 duantiles are 1.78 1.81 1.83 . |al 025, 500. 975 guantiles are -4.34-3.97-361 - -T * 025 ...
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... Statistical Science Bayesian Methods for Data Analysis Third. CHAPMAN. &. HALL/CRC. Texts. in. Statistical. Science. Series. Series Editors Julian J. Faraway, University of Bath, UK Analysis of Failure and Survival Data P. J. Smith The ...
... Statistical Science Bayesian Methods for Data Analysis Third. CHAPMAN. &. HALL/CRC. Texts. in. Statistical. Science. Series. Series Editors Julian J. Faraway, University of Bath, UK Analysis of Failure and Survival Data P. J. Smith The ...
Page iii
Bradley P. Carlin, Thomas A. Louis. Texts in Statistical Science Bayesian Methods for Data Analysis Third Edition Bradley P. Carlin Univesity of Minnesota Minneapolis, MN, U.S.A. Thomas A. Louis Johns Hopkins Bloomberg School of Public ...
Bradley P. Carlin, Thomas A. Louis. Texts in Statistical Science Bayesian Methods for Data Analysis Third Edition Bradley P. Carlin Univesity of Minnesota Minneapolis, MN, U.S.A. Thomas A. Louis Johns Hopkins Bloomberg School of Public ...
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Bradley P. Carlin, Thomas A. Louis. to Caroline, Samuel, Joshua, and Nathan and Germaine, Margit, and Erica Contents Preface to the Third Edition 1 Approaches for statistical Dedication.
Bradley P. Carlin, Thomas A. Louis. to Caroline, Samuel, Joshua, and Nathan and Germaine, Margit, and Erica Contents Preface to the Third Edition 1 Approaches for statistical Dedication.
Page vii
... statistical inference 1 1.1 1.2 1.3 1.4 1.5 1.6 2 The 2.1 2.2 2.3 2.4 2.5 Introduction 1 Motivating vignettes 2 1.2.1 Personal probability 2 1.2.2 Missing data 2 1.2.3 Bioassay 3 1.2.4 Attenuation adjustment 4 Defining the approaches 4 ...
... statistical inference 1 1.1 1.2 1.3 1.4 1.5 1.6 2 The 2.1 2.2 2.3 2.4 2.5 Introduction 1 Motivating vignettes 2 1.2.1 Personal probability 2 1.2.2 Missing data 2 1.2.3 Bioassay 3 1.2.4 Attenuation adjustment 4 Defining the approaches 4 ...
Contents
1 | |
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 |
References | 487 |
Back cover | 521 |
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Bayesian Methods for Data Analysis, Third Edition Bradley P. Carlin,Thomas A. Louis No preview available - 2008 |
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