TMA4300 Computer Intensive Statistical Methods spring 2022

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 Lecture Notes R code Reading Extra material
05.04 EM algorithm Slides Notes
13 29.03 Boorstrap, Slides Notes GH: 9.3.1, 9.5.1, 9.5.2 (until 9.5.2.3 included A visual introduction to permutation test
12 25.03 Bootstrap Slides Notes boot_example.R, regression and paired bootstrap
12 22.03 Bootstrap Slides Notes GH: 9.1, 9.2
9 04.03 R-INLA Slides
9 01.03 INLA Slides Debugging in R
8 25.02 MCMC and Gibbs sampling, congergence diagnostics, INLA Slides Taylor approximations https://arxiv.org/abs/1907.01248
8 22.02 MCMC and Gibbs sampling Slides Notes Viking, simple linear regression GL:5.3, GH:7.2,7.3
7 18.02 The Metropolis-Hastings algorithm and Gibbs sampling Slides Notes Simple 2D MCMC Illustration, RW Metropolis, Rao example GL: 6.4, 5.1, 5.2, GH: 7.1-7.2
7 15.02 More bayesian statistics, MCMC Slides Notes Toy Metropolis Hastings GL: 6.1-6.2, GH: 7.1
4 28.01 Intro to Bayesian statistics Slides Notes
4 25.01 Monte Carlo integration and Importance Sampling Slides Notes Monte Carlo integration, Importance Sampling GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1
3 21.01 Rejection sampling, adaptive rejection sampling, Monte Carlo integration, Slides Notes Code used in the lecture GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1
3 18.01 Methods based on mixtures, multivariate Normal, Rejection sampling Slides Code used in the lecture GL: 1. 4, 1.5.1, GH:6.2.2, 6.2.3
2 14.01 Bivariate techniques (Box-Muller algorithm), ratio-of-uniforms method Slides Code used in the lecture GL: 1.3.2, GH: 6-6.2.2
2 11.01 Intro to the course. Simulation of discrete RV Slides GL: 1.1-1.2-1.3.1, GH: 1 (repetition)
2022-04-06, Sara Martino