Entry requirements

120 credits including Analysis of Regression and Variance. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• 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.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.