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).
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Course structure
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
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Schedule
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. -
Course literature
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.
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More information
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Contact
