LEADER 05714nam 2200745Ia 450 001 9910819512103321 005 20200520144314.0 010 $a1-118-39447-X 010 $a1-283-65634-5 010 $a1-118-39449-6 035 $a(CKB)2670000000261636 035 $a(EBL)1037158 035 $a(SSID)ssj0000757653 035 $a(PQKBManifestationID)11463479 035 $a(PQKBTitleCode)TC0000757653 035 $a(PQKBWorkID)10771376 035 $a(PQKB)11752191 035 $a(DLC) 2012034365 035 $a(Au-PeEL)EBL1037158 035 $a(CaPaEBR)ebr10608638 035 $a(CaONFJC)MIL396884 035 $a(CaSebORM)9781118394328 035 $a(MiAaPQ)EBC1037158 035 $a(OCoLC)813535628 035 $a(OCoLC)828687957 035 $a(OCoLC)ocn828687957 035 $a(EXLCZ)992670000000261636 100 $a20120815d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCase studies in Bayesian statistical modelling and analysis /$fedited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt 205 $a1st edition 210 $aChichester, West Sussex $cJohn Wiley & Sons Inc.$d2012 215 $a1 online resource (499 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aDescription based upon print version of record. 311 $a1-118-39432-1 311 $a1-119-94182-2 320 $aIncludes bibliographical references and index. 327 $aCase Studies in Bayesian Statistical Modelling and Analysis; Contents; Preface; List of contributors; 1 Introduction; 1.1 Introduction; 1.2 Overview; 1.3 Further reading; 1.3.1 Bayesian theory and methodology; 1.3.2 Bayesian methodology; 1.3.3 Bayesian computation; 1.3.4 Bayesian software; 1.3.5 Applications; References; 2 Introduction to MCMC; 2.1 Introduction; 2.2 Gibbs sampling; 2.2.1 Example: Bivariate normal; 2.2.2 Example: Change-point model; 2.3 Metropolis-Hastings algorithms; 2.3.1 Example: Component-wise MH or MH within Gibbs; 2.3.2 Extensions to basic MCMC; 2.3.3 Adaptive MCMC 327 $a2.3.4 Doubly intractable problems2.4 Approximate Bayesian computation; 2.5 Reversible jump MCMC; 2.6 MCMC for some further applications; References; 3 Priors: Silent or active partners of Bayesian inference?; 3.1 Priors in the very beginning; 3.1.1 Priors as a basis for learning; 3.1.2 Priors and philosophy; 3.1.3 Prior chronology; 3.1.4 Pooling prior information; 3.2 Methodology I: Priors defined by mathematical criteria; 3.2.1 Conjugate priors; 3.2.2 Impropriety and hierarchical priors; 3.2.3 Zellner's g-prior for regression models; 3.2.4 Objective priors 327 $a3.3 Methodology II: Modelling informative priors3.3.1 Informative modelling approaches; 3.3.2 Elicitation of distributions; 3.4 Case studies; 3.4.1 Normal likelihood: Time to submit research dissertations; 3.4.2 Binomial likelihood: Surveillance for exotic plant pests; 3.4.3 Mixture model likelihood: Bioregionalization; 3.4.4 Logistic regression likelihood: Mapping species distribution via habitat models; 3.5 Discussion; 3.5.1 Limitations; 3.5.2 Finding out about the problem; 3.5.3 Prior formulation; 3.5.4 Communication; 3.5.5 Conclusion; Acknowledgements; References 327 $a4 Bayesian analysis of the normal linear regression model4.1 Introduction; 4.2 Case studies; 4.2.1 Case study 1: Boston housing data set; 4.2.2 Case study 2: Production of cars and station wagons; 4.3 Matrix notation and the likelihood; 4.4 Posterior inference; 4.4.1 Natural conjugate prior; 4.4.2 Alternative prior specifications; 4.4.3 Generalizations of the normal linear model; 4.4.4 Variable selection; 4.5 Analysis; 4.5.1 Case study 1: Boston housing data set; 4.5.2 Case study 2: Car production data set; References; 5 Adapting ICU mortality models for local data: A Bayesian approach 327 $a5.1 Introduction5.2 Case study: Updating a known risk-adjustment model for local use; 5.3 Models and methods; 5.4 Data analysis and results; 5.4.1 Updating using the training data; 5.4.2 Updating the model yearly; 5.5 Discussion; References; 6 A Bayesian regression model with variable selection for genome-wide association studies; 6.1 Introduction; 6.2 Case study: Case-control of Type 1 diabetes; 6.3 Case study: GENICA; 6.4 Models and methods; 6.4.1 Main effect models; 6.4.2 Main effects and interactions; 6.5 Data analysis and results; 6.5.1 WTCCC TID; 6.5.2 GENICA; 6.6 Discussion 327 $aAcknowledgements 330 $a Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how 410 0$aWiley Series in Probability and Statistics 606 $aBayesian statistical decision theory 606 $aStatistical decision 615 0$aBayesian statistical decision theory. 615 0$aStatistical decision. 676 $a519.5/42 701 $aAlston$b Clair$01661161 701 $aMengersen$b Kerrie L$01654858 701 $aPettitt$b Anthony$g(Anthony N.)$01661162 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819512103321 996 $aCase studies in Bayesian statistical modelling and analysis$94016920 997 $aUNINA