TMA 4300 Computer Intensive Statistical Methods
Lecture Plan
Note: The slides provided here might change after they have been uploaded for the first time. The slides are used in the lectures and cannot replace READING the literature on the reading list.
Provisional time plan (The corresponding room is given in brackets):
| Week | Tuesday 12:15-14:00 | Thursday 12:15-14:00 | Thursday 16:15-17:00 |
|---|---|---|---|
| 2 | 07.01. - | 09.01. Lecture (R21) | 09.01. - |
| 3 | 14.01. Lecture (F2) | 16.01. Lecture (R21) | 16.01. - |
| 4 | 21.01. Lecture (F2) | 23.01. Lecture (F4) | 23.01. - |
| 5 | 28.01. Lecture (F2) | 30.01. Lecture (R21) | 30.01. - |
| 6 | 04.02. Exercise 1 (Nullrommet 380A) | 06.02. Exercise 1 (Nullrommet 380A) | 06.02. - |
| 7 | 11.02. Exercise 1 (Nullrommet 380A) | 13.02. Exercise 1 (Nullrommet 380A) | 13.02. - |
| 8 | 18.02. Lecture (F2) | 20.02. Lecture (R21) | 20.02. Oral presentation (K5) |
| 9 | 25.02. Lecture (F2) | 27.02. Lecture (F4) | 27.02. Oral presentation (K5) |
| 10 | 04.03. Exercise 2 (Nullrommet 380A) | 06.03. Exercise 2 (Nullrommet 380A) | 06.03. - |
| 11 | 11.03. Exercise 2 (Nullrommet 380A) | 13.03. Exercise 2 (Nullrommet 380A) | 13.03. - |
| 12 | 18.03. Lecture (F2) | 20.03. Lecture (F4) | 20.03. Oral presentation (K5) |
| 13 | 25.03. Lecture (F2) | 27.03. Lecture (F4) | 27.03. Oral presentation (K5) |
| 14 | 01.04. Exercise 3 (Nullrommet 380A) | 03.04. Exercise 3 (Nullrommet 380A) | 03.04. - |
| 15 | 08.04. Exercise 3 (Nullrommet 380A) | 10.04. Exercise 3 (Nullrommet 380A) | 10.04. Oral presentation (K5) |
Lecture content:
Abbreviations: GH: Book by Givens and Hoeting, GL: Book by Gamerman and Lopes, HTF: Book by Hastie, Tibshirani and Friedman (see here).
| Week | Date | Topics | Slides | Reading |
|---|---|---|---|---|
| 2 | 09.01 | Introduction & Simulation from standard discrete and continuous distributions | 01handout.pdf demo-sim.R | GL: 1.1-1.3.1, GH: 1 (repetition), 6 - 6.2.2 |
| 3 | 14.01 | Simulation (Box-Muller, Ratio-of-uniforms) | 02handout.pdf | GL: 1.3.2, GH: 1 (repetition), 6 - 6.2.2 |
| 3 | 16.01 | Simulation (Methods based on mixtures, Multivariate-normal, rejection sampling) | 03handout.pdf demo-simmix.R | GL: 1.3.3 - 1.5.1, GH: 6.2.3 |
| 4 | 21.01 | Simulation (Rejection Sampling) | 04handout.pdf | GL: 1.5.1, GH: 6.2.3 |
| 4 | 23.01 | Simulation (Rejection Sampling, Weighted Resampling, Importance sampling, Start Bayes) | 05handout.pdf (extension of 04handout.pdf) demo-mvn.R | GL: 1.5 (all), 2.1, 2.2, GH: 1.5, 6.3.1, 6.4.1 |
| 5 | 28.01 | Bayesian inference | 06handout.pdf | GL: 2.1, 2.2, 2.3.1, GH: 1.5 |
| 5 | 30.01 | Review importance sampling, reflecting part 1 of the lecture, presentation of excercise 1 including some guidelines | 07handout.pdf, see also exercise page | |
| 8 | 18.02 | MCMC: Repetition Markov chains, Metropolis Hasting algorithm | p2_handout01.pdf demo_toymc.R | GH: 1.7 (repetition MC), 7.1, GL: 4.4-4.6 (repetition MC), 6.1, 6.2 |
| 8 | 20.02 | MCMC: Special types Metropolis Hastings, Gibbs sampling, Full-conditional distributions | p2_handout02.pdf (update)demo_toymc2.R demo_mcmcrw.R demo_mcmcRao.R | GH: 7.1, 7.2 GL: 6.3, 5.1, 5.2 |
| 9 | 25.02 | MCMC: Full-conditional distributions, Convergence diagnostics, Bayesian hierarchical models | p2_handout03.pdf beetle.R Power plant pump - Bayesian hierarchical model example | GH: 7.2, 7.3 GL: 6.4, 5.3, 5.4 |
| 9 | 27.02 | INLA | p2_handout04.pdf See also: www.r-inla.org | INLA book chapter INLA original paper |
| 12 | 18.03 | Classification (LDA, QDA, knn), Cross-validation | p3_handout01.pdf demo_lda.R demo_knn.R demo_cv.R | HTF: 4.1, 4.3 (page 106-112), 13.3 (463-468), 7.10 |
| 12 | 20.03 | Classification and bootstrapping | p3_handout02.pdf demo-boot.r demo-inla.R | GH: 9.1, 9.2.1 |
| 13 | 25.03 | Bootstrapping | p3_handout03.pdf demo-pairedbootstrap.R demo-permtest.Rpermdata.txt | GH: 9.2.2, 9.2.3, 9.2.4, 9.3.1, 9.5 (intro), 9.5.1, 9.5.2 (page 304-307 top), 9.8 |
| 13 | 27.03 | EM-algorithm | p3_handout04.pdf | GH: 4 (Intro), 4.1, 4.2 until page 102. Slides are based on Pawitan, Y (2001), "In all likelihood", Oxford University Press, Chapter 12 (intro), 12.1, 12.2, 12.4, 12.5. Paper copies were handed out during the lecture and can be obtained from the lecturer |