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Preface; 1. The subjective interpretation of probability; 2. Bayesian inference; 3. Point estimation; 4. Frequentist properties of Bayesian estimators; 5. Interval estimation; 6. Hypothesis testing; 7. Prediction; 8. Choice of prior; 9. Asymptotic Bayes; 10. The linear regression model; 11. Basics of Bayesian computation; 12. Hierarchical models; 13. The linear regression model with general covariance matrix; 14. Latent variable models; 15. Mixture models; 16. Bayesian model averaging and selection; 17. Some stationary time series models; 18. Some nonstationary time series models; Appendix; Index.

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