Entry requirements

120 credit points and Analysis of Regression and Variance

Learning outcomes

In order to pass the course (grade 3) the student should

be able to define Bayes formula and to derive a posteriori distributions;

be able to choose suitable informative and non-informative a priori distributions;

know how to use stochastic simulation to estimate a posteriori distributions;

be able to make correct inferenes from theoretical and estimated a posteriori distributions;

be able to choose the most suitable model for a given practical problem.

Content

Bayes formula. Informative and non-informative á priori distributions. Á posteriori distributions. Hierarchical models. Linear models. Bayesian inference. Markov Chain Monte Carlo (MCMC) methods.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination at the end of the course. Compulsory assignments and laboratory work during the course.