TMA4300 Computer Intensive Statistical Methods spring 2021
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 | R code | Reading | Extra material |
|---|---|---|---|---|---|---|
| 2 | 12.01 | Intro to the course. Simulation of discrete RV | Slides | Intro to R, Pseudo random number, Simulate a queue | GL: 1.1-1.2-1.3.1, GH: 1 (repetition) | |
| 2 | 14.01 | Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method | Slides | GL: 1.3.2, GH: 6-6.2.2 | ||
| 3 | 19.01 | Methods based on mixtures, multivariate Normal, Rejection sampling | Slides | GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3 | ||
| 3 | 21.01 | Rejection sampling, adaptive rejection sampling, Monte Carlo integration, importance sampling | Slides | GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1 | ||
| 3 | 22.01 | More on importance sampling | Slides | |||
| 4 | 26.01 | Intro to Bayesian statistics | Slides | here is a series of video about Jeffrey's prior | ||
| 6 | 11.02 | More bayesian statistics, MCMC | Slides | Toy Example with MCMC | GL: 6.1-6.2, GH: 7.1 | |
| 6 | 12.02 | The Metropolis-Hastings algorithm and Gibbs sampler | Slides | GL: 6.4, 5.1, 5.2, GH: 7.1-7.2 | ||
| 7 | 18.02 | MCMC and Gibbs sampler | Slides | GL:5.3,5.3, GH:7.2,7.3 | ||
| 8 | 23.02 | MCMC and GIbbsa sampler. Convergence diagnostic, INLA | Slides | https://arxiv.org/abs/1907.01248 | ||
| 9 | 02.03 | INLA | Slides | |||
| 9 | 04.03 | R-INLA | Slides | |||
| 10 | 12.03 | Bootstrap | Slides | GH: 9.1, 9.2 | ||
| 11 | 18.03 | Bootstrap | Slides | |||
| 12 | 23.03 | Bootstrap and permutation test | Slides | GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included | A visual introduction to permutation test | |
| 12 | 26.03 | EM algorithm | Slides | |||
| 27.04 | Summary | Slides |