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
2018-04-06, Håkon Tjelmeland