TMA4300 Computer Intensive Statistical Methods spring 2018
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 | Topic(s) | Sections in | Sections in | Pdfs | Comments |
|---|---|---|---|---|---|---|
| Givens and Hoeting | Gamerman and Lopes | |||||
| 14 | 05.04 | EM algorithm example | Example 4.2 | Introduction.pdf | This was the last lecture in the course. | |
| 13 | Easter holiday | |||||
| 12 | 23.03 | Bootstrapping for the bias of apparent misclassification rate | interactive6.pdf | |||
| 12 | 22.03 | Bias correction, bootstrap confidence intervals, permutation tests, the EM algorithm | 9.3.1, 9.3.2.2, 9.8, 4.1,4.2, 4.2.1 | IntroductionAndMore.pdf | ||
| 12 | 19.03 | Paramtric bootstrapping, bootstrapping of time series and regression data, bootstrapping of bias. | 9.2, 9.5.1 | Introduction.pdf | ||
| 11 | 16.03 | Empirical distribution, plug-in principle, non-parametric bootstrapping. | 9.1, 9.2 | Interactive5.pdf | ||
| 11 | 15.03 | Empirical distribution, plug-in principle, non-parametric bootstrapping. | 9.1, 9.2 | Introduction.pdf | ||
| 10 | Work with Exercise 2 | |||||
| 9 | Work with Exercise 2 | |||||
| 8 | 22.02 | Improper priors, credible intervals, conjugate prior distributions | 1.5 | 5.3.3, 2.3 | IntroductionAndMore.pdf | Last lecture in part 2. |
| 8 | 19.02 | Combination of strategies, convergence diagnostics, variance estimation | 7.2.4, 7.2.5,,7.3 | 5.3.3, 5.3.4, 5.3.5, 5.4, 6.4 | IntroductionAndMore.pdf | |
| 7 | 16.02 | Hierarchical Bayesian models, Metropolis-Hastings algorithm, Gibbs updates, random walk proposals, combination of proposal distributions | 7.1.2, 7.2.1, 7.2.2, 7.2.3, 7.3.1.1, 7.3.1.2, 7.3.1.3 | 6.3, 5.1, 5.2, 5.3.1-5.3.3 | Interactive4.pdf | This problem is based on Problem 3 in the exam May 2017. |
| 7 | 15.02 | Independent proposals, random walk proposals, Gibbs updates, combination of proposal distributions | 7.1.2, 7.2.1, 7.2.2, 7.2.3, 7.3.1.1, 7.3.1.2, 7.3.1.3 | 6.3, 5.1, 5.2, 5.3.1-5.3.3 | IntroductionAndExamples.pdf | |
| 7 | 12.02 | The Metropolis-Hastings algorithm, Ising model example | 7.1 | 6.1, 6.2 | IntroductionAndExample.pdf | |
| 6 | 09.02 | Toy Markov chain Monte Carlo | 7.1 | 6.1, 6.2 | Interactive3.pdf.pdf | |
| 6 | 08.02 | Introduction to Markov chain Monte Carlo (MCMC) | 7.1 | 6.1, 6.2 | IntroductionAndMore.pdf | |
| 6 | 05.02 | Oral presentations from Exercise 1 | First lecture in part 2: 08.02 | |||
| 5 | Work with Exercise 1 | |||||
| 4 | 26.01 | Work with Exercise 1 | ||||
| 4 | 22.01 | simulation from multivariate normal, Bayesian modelling | 1.4.1, 2.1, 2.2, 2.4 | introduction.pdf, bayesIntro.pdf | Read about Monte Carlo and importance sampling in Givens and Hoeting sections 6.1 and 6.4.1. | |
| 3 | 19.01 | rejection sampling, simulation using known relations | 6.2.3 | 1.5.1 | Interactive2.pdf | |
| 3 | 15.01 | methods best on mixtures, rejection sampling, adaptive rejection sampling | 6.2.3, 6.2.3.2 | 1.3.3, 1.5.1, 1.5.3 | Introduction.pdf | |
| 2 | 12.01 | probability integral transform, Box-Muller | 6.2.1, 6.2.2 | 1.3.1, 1.3.2 | Interactive1.pdf | |
| 2 | 11.01 | bivariate techniques, ratio-of-uniforms method, methods based on mixtures | 6.2.1 | 1.3.2, 1.3.3 | Introduction.pdf | |
| 2 | 08.01 | Introduction, pseudo-random generators, simulation from discrete distributions, probability integral transform | 6.2, 6.2.1, 6.2.2 | 1.1, 1.2, 1.3.1 | Introduction.pdf |