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 |