Computational Methods for Bayesian Statistics

Education facts

Code: MSA102

The course gives an introduction to Bayesian statistical modelling and inference, focusing on computational methods such as Markov chain Monte Carlo (McMC) and other simulation methods, but also looking at tools such the EM algorithm. We emphasize the interplay between statistical modeling and applied problem solving, as well as computational and theoretical aspects of the models.

Entry requirements

The course requires a strong background in mathematics, at least one course in statistics, and skills in scientific programming (for example in R or Python) as achieved by completing MSG400 "Stochastic Data Processing and Simulation".

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Computational Methods for Bayesian Statistics

  University of Gothenburg

  Start date: 31 August 2026

  End date: 3 November 2026

  Pace of study: 50 %