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
2020-04-30, Sara Martino