7,5 hp

This course introduces the Bayesian approach to statistics, with focus on model building.

The course goes through the fundementals of Bayesian statistics, like Bayes theorem, prior distribution, likelihood, posterior distribution etc. The main objective to give the students the tools for applying and understanging bayesian models for real-world applications.

The inference (computional analysis) will be done using Stan (R-stan), which is state of the art platform statistical modeling. The computer exercises will be done in R (python is allowed, however all the examples and exercises are written for R).

Course book: Statistical Rethinking by Richard McElreath

Learning outcomes:

• Undestand the basic of Bayesian analyasis.
• Interpret output from Bayesian models
• Use R, stan for basic Bayesian analysis

 

Syllabus and Course Literature