1. Introduction: The Bayesian Method, its Benefits and Implementation
2. Bayesian Model Choice, Comparison and Checking
3. The Major Densities and their Application
4. Normal Linear Regression, General Linear Models and Log-Linear Models
5. Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling 6. Discrete Mixture Priors
7. Multinomial and Ordinal Regression Models
8. Time Series Models
9. Modelling Spatial Dependencies
10. Nonlinear and Nonparametric Regression
11. Multilevel and Panel Data Models
12. Latent Variable and Structural Equation Models for Multivariate Data
13. Survival and Event History Analysis
14. Missing Data Models
15. Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations
▶Appendix
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Zumdahl,줌달,맥머리
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