TMA4300 Computer Intensive Statistical Methods spring 2017

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 Sections in Pdfs Comments
Givens and Hoeting Gamerman and Lopes Hastie, Tibshirani and Friedman
16 Exercise 3. No lectures this week.
15 Easter holiday.
14 Exercise 3. No lectures this week.
13 31.03 Bootstrapping: confidence interval, prediction error. Permutation tests. 9.3.1, 9.3.2.2, 9.8. IntroductionAndMore.pdf
13 27.03 Paramtric bootstrapping, bootstrapping of time series and regression data, bootstrapping of bias. 9.2, 9.5.1 Introduction.pdf
12 24.03 Emperical distribution, plug-in principle, non-paramtric bootstrapping. 9.1, 9.2.1 Introduction.pdf
12 20.03 \(k\)-nearest neighbour classifier, cross validation. 13.3 (p. 463-468), 7.10 IntroductionAndMore.pdf
11 13.03 Classification, LDA, QDA. 4.1, 4.3 (p. 106-112) IntroductionAndMore.pdf Remember the oral presentations on Friday this week.
10 Exercise 2. No lectures this week.
9 03.03 Exercise 2. No lecture.
9 02.03 k-parameter exponential distributions and conjugate prior distributions, conditional conjugacy. 2.3.1, 2.3.3 IntroductionAndMore.pdf
9 27.02 Exercise 2. No lecture.
8 23.02 One-parameter exponential family and conjugate prior distributions. 2.3.1 Introduction.pdf
8 20.02 Convergence diagnostics, comparison of algorithms, typical MCMC problems, variance estimation, improper priors. 7.2.4, 7.2.5, 7.3 5.3.3, 5.3.4, 5.3.5, 5.4, 6.4 IntroductionAndMore.pdf Remember the lecture on Thursday this week.
7 17.02 Combination of strategies, Gibbs sampler, Convergence diagnostics. 7.2.1-7.2.3, 7.3.1.1, 7.3.1.2, 7.3.1.6, 7.3.2 5.1, 5.2, 5.3.1, 5.3.2, 5.4 IntroductionAndExamples.pdf
7 13.02 Metropos-Hastings for continuous distributions, Independent proposals, random walk proposals, combination of strategies. 7.1.1, 7.1.2 6.3 IntroductionAndExamples.pdf Remember the oral presentations on Thursday this week.
6 10.02 The Metropolis-Hastings algorithm. 7.1 6.1, 6.2 IntroductionAndIsingExample.pdf
6 06.02 Introduction to Markov chain Monte Carlo (MCMC). 7.1 6.1, 6.2 IntroductionAndExample.pdf Remember the oral presentations on Thursday this week.
4-5 Exercise 1. No lectures these two weeks.
3 20.01 Introduction to Bayesian modelling and hierarchical Bayesian modelling. 2.1, 2.2, 2.4 Information.pdf, bayesIntro.pdf
3 19.01 Adaptive rejection sampling, Monte Carlo integration, importance sampling. 6.2.3.2, 6.1, 6.4.1 1.5.3 introduction.pdf
3 16.01 Simulation from multivariate normal distributions, rejection sampling. 6.2.3 1.4, 1.5.1 introduction.pdf See message dated 16.01 at Home
2 13.01 Bivariate techniques, Box-Muller, ratio-of-uniforms method, methods based on mixtures. 1.3.2, 1.3.3 introduction.pdf
2 12.01 Simulation from continuous distribution: inversion method, gamma distribution, bivariate techniques. 6.2.1, 6.2.2 1.3.1, 1.3.2 introduction.pdf
2 09.01 Introduction, simulation from discrete distributions. 6.1 1.1, 1.2 introduction.pdf
2017-03-31, Håkon Tjelmeland