2nd Edition. Wiley series in probability and statistics. Wiley &
Sons, Ltd, 2006. - 598 pages.
Contents:
Introduction: The Bayesian Method, its Benefits and Implementation
Bayesian Model Choice, Comparison and Checking
The Major Densities and their Application
Normal Linear Regression, General Linear Models and Log-Linear Models
Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling
Discrete Mixture Priors
Multinomial and Ordinal Regression Models
Time Series Models
Modelling Spatial Dependencies
Nonlinear and Nonparametric Regression
Multilevel and Panel Data Models
Latent Variable and Structural Equation Models for Multivariate Data
Survival and Event History Analysis
Missing Data Models
Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations
Appendix: A Brief Guide to Using WINBUGS
Contents:
Introduction: The Bayesian Method, its Benefits and Implementation
Bayesian Model Choice, Comparison and Checking
The Major Densities and their Application
Normal Linear Regression, General Linear Models and Log-Linear Models
Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling
Discrete Mixture Priors
Multinomial and Ordinal Regression Models
Time Series Models
Modelling Spatial Dependencies
Nonlinear and Nonparametric Regression
Multilevel and Panel Data Models
Latent Variable and Structural Equation Models for Multivariate Data
Survival and Event History Analysis
Missing Data Models
Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations
Appendix: A Brief Guide to Using WINBUGS