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 random variate generation and posterior simulation

12. Posterior simulation via Markov chain Monte Carlo

13. Hierarchical models

14. Latent variable models

15. Mixture models

16. Bayesian methods for model comparison, selection and big data

17. Univariate time series methods

18. State space and unobserved components models

19. Time series models for volatility

20. Multivariate time series methods

Appendix

Bibliography

Index

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 random variate generation and posterior simulation

12. Posterior simulation via Markov chain Monte Carlo

13. Hierarchical models

14. Latent variable models

15. Mixture models

16. Bayesian methods for model comparison, selection and big data

17. Univariate time series methods

18. State space and unobserved components models

19. Time series models for volatility

20. Multivariate time series methods

Appendix

Bibliography

Index