Swedish web site
732A46 Bayesian Learning
Timetable
Here is the schedule in TimeEdit
BDA = Bayesian Data Analysis book
VN = Von Neumann
AT = Alan Turing
Here is the schedule in TimeEdit
BDA = Bayesian Data Analysis book
VN = Von Neumann
AT = Alan Turing
| What? | When? | Where? | Read? | Contents | Material |
| Lecture 1 | Oct 30, 10-12 | VN | BDA, Ch. 1-2. | Likelihood. Intro to Bayesian inference. Bernoulli model. | Slides 1/page Slides 4/page |
| Lecture 2 | Oct 30, 13-15 | VN | BDA, Ch. 1-2. | Normal and Poisson models. Conjugate and non-informative priors. | Slides 1/page Slides 4/page |
| Lecture 3 | Oct 31, 10-12 | VN | BDA, Ch. 3.1-3.3 and 3.5-3.6 | Multiparameter models, Marginalization, Normal with unknown variance, Multinomial, Multivariate normal | Slides 1/page Slides 4/page |
| Exercise 1 | Oct 31, 13-15 | VN | Exercise 1.1, 1.6, 2.1, 2.5, 2.8, 2.11, 2.14 in BDA |
Photocopies of the exercises Solutions More solutions - scans in Swedish. |
|
| Lecture 4 | Nov 7, 10-12 | VN | Prediction and making decisions | Slides 1/page Slides 4/page |
|
| Exercise 2 | Nov 7, 13-15 | VN | 2.11, 4.1, 2.13, 2.19, 2.22 in BDA. | Solutions | |
| Lecture 5 | Nov 8, 10-12 | VN | BDA 4.1-4.2, 14.1-3 and 14.6-7. | Linear regression. Binary regression. Posterior approximation. | Slides 1/page Slides 4/page |
| Computer 1 | Nov 8, 13-15 | PC4, PC5 | Gridding posteriors. Basic simulation. | Lab 1 | |
| Lecture 6 | Nov 13, 10-12 | VN | Splines. Shrinkage priors. Variable selection. | Slides 1/page Slides 4/page |
|
| Exercise 3 | Nov 13, 13-15 | VN | 3.1, 3.2, 3.5 in BDA. | Solutions | |
| Lecture 7 | Nov 14, 10-12 | VN | Chapter 5.1-5.5 | Exchangeability. Hierarchical models. | Slides 1/page Slides 4/page |
| Computer 2 | Nov 14, 13-15 | PC4, PC5 | Bayesian polynomial regression with conjugate prior. | Lab 2 JapanTemp.dat |
|
| Lecture 8 | Nov 27, 10-12 | VN | BDA Ch. 10-11 | Gibbs sampling and data augmentation | Slides 1/page Slides 4/page |
| Exercise 4 | Nov 27, 13-15 | VN | 3.12, 14.3, 14.4, 14.7 in BDA. | ||
| Lecture 9 | Nov 28, 10-12 | VN | BDA Ch. 10-11 | Markov Chain Monte Carlo, Metropolis-Hastings | Slides 1/page Slides 4/page |
| Computer 3 | Nov 28, 13-15 | PC4, PC5 | Gibbs sampling and binary regression. | Lab 3 CanadianWages.dat |
|
| Lecture 10 | Dec 5, 10-12 | VN | Live OpenBugs session | No slides | |
| Exercise 5 | Dec 5, 13-15 | VN | 11.3 (but based on exercise 3.12) | ||
| Lecture 11 | Dec 6, 10-12 | VN | Article on Bayes factors | Model inference | Slides 1/page Slides 4/page |
| Computer 4 | Dec 6, 13-15 | PC4, PC5 | The Metropolis algorithm | Lab 4 - new version dbetaLogit.R |
|
| Lecture 12 | Dec 7, 10-12 | AT | Bayesian variable selection and Summary | Slides 1/page Slides 4/page |
Note: Linköping University makes use of so called academic quarters.
Page responsible: Mattias Villani
Last updated: 2012-12-07
