Thursday 25 August
- A general introduction to the course was given, and we went through some of the core material of Chapter 1.1-1.3
- Lecture notes
- Simple example
Thursday 1 September
- We finished the introduction based on key aspects of Chapter 1
- Lecture notes
Thursday 8 September
- Vegard went through 2.1 and 2.2. Lecture notes
- We start chapter 2 on single-parameter models, please read 2.1-2.3
- I went through 2.3 combined with the basics from exercise 2 in 'Course Notes and Exercises by Nils Lid Hjort
Thursday 15 September
- We talked about informative priors for single-parameter models
- Lecture notes
- The asthma mortality rate example is in this note (R-scripts: little data and more data)
Thursday 22 September
- Introduction to multiparameter models
- Lecture notes
Thursday 29 September
- Non-informative prior distributions
- More conjugate, multiparameter models
- Lecture notes
Thursday 6 October
- The conjugate multinomial-Dirichlet model
- Started asymptotic theory for Bayesian analysis (ch. 4)
- Lecture notes
- The US election poll and asthma mortality rate examples are in this note
Thursday 13 October: Lecture cancelled
Thursday 20 October
- Sketch of proof of the normal approximation
- Intro hierarchical modelling, start with a real MCMC (Gibbs-)- simulation example
- Lecture notes
- The R-script for the Gibbs sampling is here
Thursday 27 October
- More on constructing hierarchical models (Ch 5.3 of the textbook). Lecture notes
- Briefly on the empirical Bayes approach (not in the textbook). Lecture notes in this note
- Peter Müller, who is Professor at The Mathematics Department at the University of Texas at Austin, is visiting us this semester and is going to give a lecture on material from chapter 10 of the textbook. Peter's lecture notes
Thursday 3 November
- Intro to Markov Chain Monte Carlo (MCMC)
- Lecture notes
Thursday 10 November
- Continue Markov Chain Monte Carlo:
- The Metropolis-Hastings algorithm, and special cases (11.2-11.3)
- Inference and check of convergence (11.4)
- Intro to Bayesian regression modelling (approximately 14.1-14.2 and 16.1)
- Lecture notes
- R-script for the simple textbook Metropolis example, recreating approximately Fig11.1
Thursday 17 November
- More about regression models (but the majority of time spent on exercises)
- Lecture notes
Thursday 24 November
- More on MCMC, including effective number of independent simulation draws neff (but not the estimation of the autocorrelations, and hence not the estimation of neff)
- A brief overview of more efficient MCMC algorithms and alternatives to MCMC (not part of the curriculum)
- Lecture notes
Thursday 1 December
- 13.15-max 14.30: Open lecture, for example questions from the students