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 |