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) |