Bayesian Methods
A course in Bayesian methods.
The course covers Bayes' formula, informative and non-informative prior distributions, posterior distributions, single- and multiparameter distributions such as binomial, multinomial och normal distributions, hierarchical models, linear models, Bayesian inference, goodness-of-fit measures and stochastic simulation with MCMC (Markov Chain Monte Carlo).
The course consists of one module.
Teaching Format
Teaching consists of lectures, exercises, and computer assignments. A passing final grade requires participation in computer exercises.
Assessment
The course is assessed through written examination.
Examiner
A list of examiners can be found on
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.
Carlin & Louis: Bayesian methods for data analysis. CRC Press.
Course reports are displayed for the three most recent course instances.
