TMA4300 Computer Intensive Statistical Methods spring 2022
Lecture log
Below you find an overview of what we have discussed in each lecture. This overview will be updated after each lecture.
| Week | Date | Topics | Slides | Lecture Notes | R code | Reading | Extra material |
|---|---|---|---|---|---|---|---|
| 05.04 | EM algorithm | Slides | Notes | ||||
| 13 | 29.03 | Boorstrap, | Slides | Notes | GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included | A visual introduction to permutation test | |
| 12 | 25.03 | Bootstrap | Slides | Notes | boot_example.R, regression and paired bootstrap | ||
| 12 | 22.03 | Bootstrap | Slides | Notes | GH: 9.1, 9.2 | ||
| 9 | 04.03 | R-INLA | Slides | ||||
| 9 | 01.03 | INLA | Slides | Debugging in R | |||
| 8 | 25.02 | MCMC and Gibbs sampling, congergence diagnostics, INLA | Slides | Taylor approximations | https://arxiv.org/abs/1907.01248 | ||
| 8 | 22.02 | MCMC and Gibbs sampling | Slides | Notes | Viking, simple linear regression | GL:5.3, GH:7.2,7.3 | |
| 7 | 18.02 | The Metropolis-Hastings algorithm and Gibbs sampling | Slides | Notes | Simple 2D MCMC Illustration, RW Metropolis, Rao example | GL: 6.4, 5.1, 5.2, GH: 7.1-7.2 | |
| 7 | 15.02 | More bayesian statistics, MCMC | Slides | Notes | Toy Metropolis Hastings | GL: 6.1-6.2, GH: 7.1 | |
| 4 | 28.01 | Intro to Bayesian statistics | Slides | Notes | |||
| 4 | 25.01 | Monte Carlo integration and Importance Sampling | Slides | Notes | Monte Carlo integration, Importance Sampling | GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1 | |
| 3 | 21.01 | Rejection sampling, adaptive rejection sampling, Monte Carlo integration, | Slides | Notes | Code used in the lecture | GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1 | |
| 3 | 18.01 | Methods based on mixtures, multivariate Normal, Rejection sampling | Slides | Code used in the lecture | GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3 | ||
| 2 | 14.01 | Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method | Slides | Code used in the lecture | GL: 1.3.2, GH: 6-6.2.2 | ||
| 2 | 11.01 | Intro to the course. Simulation of discrete RV | Slides | GL: 1.1-1.2-1.3.1, GH: 1 (repetition) |