TMA4300 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 | Monday 14:15-15:00 | Tuesday 10:15-12:00 | Thursday 16:15-18:00 |
|---|---|---|---|
| 2 | 11.01. - | 12.01. Lecture (S5) - Part 1 | 14.01. Lecture (R10) - Part 1 |
| 3 | 18.01. - | 19.01. Lecture (S5) - Part 1 | 21.01. Lecture (R10) - Part 1 |
| 4 | 25.01. - | 26.01. Lecture (S5) - Part 1 | 28.01. Lecture (R10) - Part 1 |
| 5 | 01.02. - | 02.02. Exercises (Nullrommet 380A) - Part 1 | 04.02. Exercises (Nullrommet 380A) - Part 1 |
| 6 | 08.02. - | 09.11. Exercises (Nullrommet 380A) - Part 1 | 11.02. Exercises (Nullrommet 380A) - Part 1 |
| 7 | 15.02. Oral presentation (F2) | 16.02. Lecture (S5) - Part 2 | 18.02. Lecture (R10) - Part 2 |
| 8 | 22.02. Oral presentation (F2) | 23.02. Lecture (S5) - Part 2 | 25.02. Lecture (R10) - Part 2 |
| 9 | 29.02. - | 01.03. Lecture (S5) - Part 2 | 03.03. Lecture (R10) - Part 2 |
| 10 | 07.03. - | 08.03. Lecture (S5) - Part 3 | 10.03. Exercises (Nullrommet 380A) - Part 2 |
| 11 | 14.03. - | 15.03. Exercises (Nullrommet 380A) - Part 2 | 17.03. Exercises (Nullrommet 380A) - Part 2 |
| 12 | 21.03. Easter Holiday | 22.03. Easter Holiday | 24.03. Easter Holiday |
| 13 | 28.03. Easter Holiday | 29.03. Easter Holiday | 31.03. Exercises (Nullrommet 380A) - Part 2 |
| 14 | 04.04. Oral presentation (F2) | 05.04. Lecture (S5) - Part 3 | 07.04. Lecture (R10) - Part 3 |
| 15 | 11.04. Oral presentation (F2) | 12.04. Lecture (S5) - Part 3 | 14.04. Exercises (Nullrommet 380A) - Part 3 |
| 16 | 18.04. - | 19.04. Exercises (Nullrommet 380A) - Part 3 | 21.04. Exercises (Nullrommet 380A) - Part 3 |
| 17 | 25.04. - | 26.04. Oral presentation & Summary S5 | 28.04. — |
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 | 12.01 | Organizationalt things, Introduction to R and simulations | Slides , Notes from document camera , Introduction to R , R-script for random number generator | GL: 1.1, GH: 1 (repetition) |
| 2 | 14.01. | Review random variables + prob.distributions, sampling from discrete distributions, inversion method, bivariate techniques (Box-Muller), … | Slides , R-script for sampling from a Bernoulli and binomial distribution | GL: 1.2-1.3.2, GH: 6-6.2.2 |
| 3 | 19.01 | Box-Muller algorithm, ratio-of-uniforms | Slides , Notes from document camera | GL: 1.3.2, 1.3,3, 1.4 (all), GH: 6-6.2.2 |
| 3 | 21.01. | Method based on mixtures, rejection sampling | Slides , R-script for sampling from a mixture distribution, R-script to illustrate rejection sampling, R-script to simulate from a Student-t distribution using the method of mixtures UPDATE: made R-code available | GL: 1.3,3, 1.4 (all), 1.5.1, GH: 6.2.3 |
| 4 | 26.01 | Finish rejection sampling, adaptive rejection sampling, Monte Carlo integration, importance sampling | See slides last time + Slides , Notes from document camera | GL: 1.5 (all), GH: 6.2.3 (all), 6.3.1, 6.4.1 |
| 4 | 28.01 | Finish importance sampling and introduction to Bayes | See slides last time + Slides , R-script to illustrate Monte Carlo integration and importance sampling | GL: 2.1, 2.2, GH: 1.5 |
| 7 | 16.02 | Introduction to Bayesian inference II | Slides , Notes from document camera | GL: 2.3 intro, 2.3.1, GH: 1.5 |
| 7 | 18.02 | Review: Markov chains and start Markov chain Monte Carlo | Slides , R-script to sample from a Poisson distribution using MCMC. | GL: 4.1-4.6 (repetition MC), 6.1, 6.2, 6.3, GH: 1.7 (repetition MC), 7 intro, 7.1 |
| 8 | 23.02 | Metropolis-Hasting algorithm and Gibbs sampling | Notes from document camera, Slides , R-script to illustrate random walk propsals, Rao example comparing random walk and independence proposals. | GL: 6.4, 5.1, 5.2, GH: 7.1-7.2 |
| 8 | 25.02 | Review Gibbs sampling + Convergence diagnostics | Slides , R-script to illustrate convergence checks, R-script to compute effective sample size (ESS), R-script to reproduce beetle example | GH: 7.2, 7.3 |
| 9 | 01.03 | Integrated nested Laplace approximation (INLA) | Notes from document camera, Slides Intro to INLA, Slides R-package INLA | |
| 9 | 03.03 | Continuation INLA + presenation of project 2 | See slides last time, R-code for simple INLA example that is shown in slides, R-code example for defining a Besag model in INLA | INLA webpage with documentation |
| 10 | 08.03 | Classification (LDA, QDA, KNN) and cross-validation | Notes from document camera, Slides, Example for LDA, Illustration of KNN, Illustration of cross-validation | HTF: 4.1, 4.3 (page 106-112), 13.3 (463-468), 7.10 |
| 14 | 05.04 & 07.04 | Bootstrapping | Notes from document camera on 5th of April, Slides, Illustration of bootstrapping, Illustration of paired bootstrapping, Illustration of permutation test, Data needed for permutation test example | GH: 9.1, 9.2, 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 |
| 15 | 12.04 | EM-algorithm | Notes from document camera on 12th of April, Slides, Illustration how to do first EM-example with modified design matrix, Lecture based on chapter 12 (intro), 12.1, 12.2 and 12.4 of Pawitan "In all likelihood", 2014, OUP press. (available for download within NTNU, just search for "Pawitan" in the NTNU online library and follow the link to "ebrary Academic Complete") | GH: 4 (Intro), 4.1, 4.2 until page 102. |
| 17 | 26.04 | Oral presentations of Project 3 and short course summary + question session | Slides |