This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.
This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.
Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.
Inhaltsangabe
Contents Preface 1. Bayesian Methods for Complex Data: Estimation and Inference 2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan 3. Model Fit, Comparison, and Checking 4. Borrowing Strength via Hierarchical Estimation 5. Time Structured Priors 6. Representing Spatial Dependence 7. Regression Techniques Using Hierarchical Priors 8. Bayesian Multilevel Models 9. Factor Analysis, Structural Equation Models, and Multivariate Priors 10. Hierarchical Models for Longitudinal Data 11. Survival and Event History Models 12. Hierarchical Methods for Nonlinear and Quantile Regression
Contents Preface 1. Bayesian Methods for Complex Data: Estimation and Inference 2. Bayesian Analysis Options in R, and Coding for BUGS, JAGS, and Stan 3. Model Fit, Comparison, and Checking 4. Borrowing Strength via Hierarchical Estimation 5. Time Structured Priors 6. Representing Spatial Dependence 7. Regression Techniques Using Hierarchical Priors 8. Bayesian Multilevel Models 9. Factor Analysis, Structural Equation Models, and Multivariate Priors 10. Hierarchical Models for Longitudinal Data 11. Survival and Event History Models 12. Hierarchical Methods for Nonlinear and Quantile Regression
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