Author Description

The Second Edition Of Bayesian Data Analysis Continues To Emphasize Practice Over Theory, Clearly Describing How To Conceptualize, Perform, And Critique Statistical Analyses From A Bayesian Perspective. Its World-class Authors Provide Detailed Guidance On All Aspects Of Bayesian Data Analysis And Include Many Examples Of Real Statistical Analyses, Based On Their Own Research. Part I: Fundamentals Of Bayesian Inference -- Background -- Single-parameter Models -- Introduction To Multiparameter Models -- Large-sample Inference And Frequency Properties Of Bayesian Inference -- Part Ii: Fundamentals Of Bayesian Data Analysis -- Hierarchical Models -- Model Checking And Improvement -- Modeling Accounting For Data Collection -- Connections And Challenges -- General Advice -- Part Iii: Advanced Computation -- Overview Of Computation -- Posterior Simulation -- Approximations Based On Posterior Modes -- Special Topics In Computation -- Part Iv: Regression Models -- Introduction To Regression Models -- Hierarchical Linear Models -- Generalized Linear Models -- Models For Robust Inference -- Part V: Specific Models And Problems -- Mixture Models -- Multivariate Models -- Nonlinear Models -- Models For Missing Data -- Decision Analysis -- Appendixes. Standard Probability Distributions -- Outline Of Proofs Of Asymptotic Theorems -- Example Of Computation In R And Bugs. Andrew Gelman... [et Al.]. Includes Bibliographical References (p. 611-646) And Indexes.