Entry requirements

120 credits including 90 credits in mathematics. Participation in Regression Analysis and Inference Theory II or participation in Introduction to Data Science. 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:

• choose suitable informative and non-informative prior distributions;
• derive posterior distributions;
• apply computer intensive methods for approximating the posterior distribution using R;
• be able to interpret the results obtained by Bayesian methods.

Content

The choice of prior distributions. Conjugate families. Bayesian point estimation. Bayesian tests. MCMC. Gibbs sampler. Bayesian model choice.

Instruction

Lectures and computer sessions, projects.

Assessment

Written examination (4 credits) at the end of the course. Compulsory assignments (1 credit) and projects (2,5 credits) 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.

Other directives

This course cannot be included in the same degree as 1MS900.