Bayesian Methods

A course in Bayesian methods.

The course covers basic and modern Bayesian methods. These include informative and non-informative prior distributions, posterior distributions, hierarchical models, Bayesian inference, model assessments, Bayesian computation and sampling with MCMC (Markov Chain Monte Carlo). The course also includes an overview of some of the following modern topics: variational Bayes, non-parametric Bayesian methods, causal inferences and Bayesian networks.

This course replaces an earlier version with course code MT7045.

The course consists of two modules, theory and hand-in assignments.

Teaching Format

Instruction is given in the form of lectures, exercise sessions and supervision.

Assessment

Assessment takes place through a written exam, and hand-in assignments.

Examiner

The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Note that the course literature can be changed up to two months before the start of the course.
Course reports are displayed for the three most recent course instances.

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