TMA4300 Computer Intensive Statistical Methods spring 2020
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 | 07.01 | Organisation of the course. Intro to R. Simulation from discrete RV | Slides | Intro to R, Pseudo random number, Simulate a queue | GL: 1.1-1.2-1.3.1, GH: 1 (repetition) | Brief history of random number generation |
| 2 | 09.01 | Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method | Slides | GL: 1.3.2, GH: 6-6.2.2 | ||
| 3 | 14.01 | Methods based on mixtures, multivariate Normal, Rejection sampling | Slides | Code to illustrate ratio of uniform method, Code to illustrate rejection sampling | GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3 | |
| 3 | 16.01 | 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 | ||
| 4 | 21.01 | Slides | ||||
| 7 | 13.02 | Hierarchical Bayesian modelling, Markov chain Monte Carlo (MCMC) | bayesIntro.pdf, mcmcIntro.pdf | GL: 6.1-6.2, GH: 7.1 | ||
| 7 | 14.02 | The Metropolis-Hastings algorithm, construction of the Markov chain | Slides | GL: 2.3-2.4, 6.1-6.2, GH:7.1 | ||
| 8 | 18.02 | The Metropolis-Hastings algorithm and Gibbs sampler | Slides | R code to illustrate RW proposal, R code for the Rao example | GL: 6.4, 5.1, 5.2, GH: 7.1-7.2 | |
| 8 | 20.02 | The Metropolis-Hastings algorithm and Gibbs sampler. Convergence diagnostic | Slides | GL:5.3,5.3, GH:7.2,7.3 | ||
| 8 | 21.02 | Integrated Nested Laplace Approximation (INLA) | Slides | paper about inla, A gentle introduction to INLA | ||
| 9 | 25.02 | INLA | Slides, R-inla library | |||
| 11 | 12.03 | Bootstrap | Slides | GH: 9.1, 9.2 | ||
| 12 | 17.03 | Boorstrap | Slides, iPad Notes | INtro to bootstrap, Bootstrap regression | ||
| 12 | 19.03 | Bootstrap and permutation test | Slides, iPad Notes | GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included | ||
| 13 | 24.03 | EM algorithm | Slides, iPad Notes | |||
| 14 | 30.04 | Summary and info about the exam | Slides |