After taking this course, you will understand the difference between various interpretations of probability and be able to formulate a statistical problem on the basis of a Bayesian perspective. You will both learn to solve standard statistical problems using Bayesian methods and to solve statistical problems using simulation-based computational methods, such as the Markov Chain Monte Carlo (MCMC), which are often used in Bayesian inference.

In Bayesian inference, parameters are considered to be random variables and any previous knowledge about these parameters is expressed as a probability distribution, the so called a priori distribution. This prior distribution is then updated to a posterior distribution by using Bayes’ theorem to combine it with the observed data which is expressed through the likelihood function. The a posterior distribution, thus, expresses evidence about the parameters after data has been observed.

Special eligibility requirements: 90 Higher Education Credits (HEC) in Statistics or equivalent. English B or equivalent.
Language: English

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Course information

More information for registered students will be found in Athena.

Teachers autumn 2019

Course coordinator

Mattias Villani

Teaching assistant

Oscar Oelrich