Syllabus
- Bayesian modeling and simple models
- Chapter 2-3 and 5 in lecture notes
- Chapter 1 and 2.1-2.2 in book
- Monte Carlo integration
- Chapter 4 in lecture notes
- Chapter 3.1 and 3.3 in book
- Model selection
- Chapter 6 in lecture notes
- Chapter 2.3.3 in book
- Markov Chain Monte Carlo algorithms
- Chapter 7 in lecture notes
- Chapter 3.4.1-3.4.2 in book
- Diagnosing convergence
- Chapter 8 in lecture notes
- Chapter 3.4.5-3.4.6 in book
- Asymptotic methods
- Chapter 9 in lecture notes
- Chapter 3.2 in book
- The multivariate normal model
- Chapter 10 in lecture notes
- Linear regression, group comparisons and hierarchical modeling
- Chapter 11-12 in lecture notes
- Chapter 2.4.1 and 4.1 in book
- Decision theory
- Chapter 13 in lecture notes
- Appendix B.1-B.3 in book
- Empirical Bayes
- Chapter 14 in lecture notes
- Chapter 5.1-5.2 in book?
- All problems from Problem Sets 1-11
Main textbook
Carlin and Louis (2009): Bayesian methods for data analysis (Third edition), Chapman & Hall, ISBN 978-1-58488-697-6
Additional text
Robert and Casella (2010): Introducing Monte Carlo Methods with R, Springer ISBN: 978-1-4419-1575-7 (Print) 978-1-4419-1576-4 (Online)
Available online on http://link.springer.com/book/10.1007/978-1-4419-1576-4/page/1
Software
For computer exercises, we will use the freely available statistical programming language R, see http://www.r-project.org/.