TMA4300 Computer Intensive Statistical Methods spring 2019
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 | Extra Material | Reading |
|---|---|---|---|---|---|
| 2 | 08.01 | Organisation of the course. Intro to R. Simulation from discrete RV | Slides | R code for Simulation of Queue, R code for simulation of pseudo random numbers, Intro to R , Some history about random number generators] | GL: 1.1-1.2-1.3.1, GH: 1 (repetition) |
| 2 | 10.01 | Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method | Slides | R code to illustrate inversion sampling | GL: 1.3.2, GH: 6-6.2.2 |
| 2 | 11.01 | Methods based on mixtures, multivariate Normal, Rejection sampling | Slides | R code to illustrate ratio of uniforms method and mixtures method,R code to illustrate rejection sampling | GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3 |
| 3 | 15.01 | Finish 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 | 17.01 | Finished inportance sampling, Intro to bayesian statistics | Slides | R code to illustrate importance sampling | GL: 2.1, 2.2, |
| 6 | 05.02 | More on Bayesian statistics, MCMC intro | Slides | R code to implement toy MCMC example | GL: 2.3-2.4, 6.1-6.2, GH:7.1 |
| 7 | 12.02 | Metropolis-Hasting algorithm and Gibbs sampling | 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 |
| 7 | 14.02 | Gibbs sampling, Convergence diagnostic | Slides | R code to implement Gibbs sampling for linear regression, R code to implement Gibbs sampling for the vikings example | GL: 6.4, 5.3, 5.4, GH: 7.2-7.3 |
| 7 | 15.02 | Convergence diagnostic, Intro to INLA | Slides | GL:5.3,5.3, GH:7.2,7.3 | |
| 8 | 19.02 | Integrated nested Laplace approximation (INLA) | Slides | R code to illustrate the GMRF approximation , A gentle introduction to INLA | |
| 8 | 21.02 | Integrated nested Laplace approximation (INLA) | Slides | Simple example of R-INLA , Besag model with INLA , R code from the slides | |
| 11 | 12.03 | Bootstrap | Slides | R code with intro to bootstrap | GH: 9.1, 9.2 |
| 11 | 15.03 | Bootstrap | Slides | R code with bootstrap example, R code Nickel Alloy example, R code Lutenizing hormon example | GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included) |
| 12 | 19.03 | Bootstrap and permutation test | same slide as last time | ||
| 12 | 22.03 | EM algorithm | Slides | GH: 4.1, 4.2 | |
| 11.04 | Summary | Slides |