Bayesian Learning

The course introduces basic concepts, ideas and methods of Bayesian inference. It is coordinated with a second-year master's course in the programme 'Statistics and Data Mining', and the focus with be on models and methods in the field of data mining and machine learning, but most of the course content is of general statistical interest.
The course will be in English and will be given in the period October 28 - December 5, 2013. See the course web page for a detailed schedule.

  • Introduction to subjective probability and the basic ideas behind Bayesian inference
  • Prior-to-posterior updating in basic statistical models, such as the Bernoulli, normal and multinomial models.
  • Bayesian analysis of linear and nonlinear regression models
  • Shrinkage, variable selection and other regularization priors
  • Bayesian analysis of more complex models with simulation methods, e.g. Markov Chain Monte Carlo (MCMC).
  • Bayesian prediction and marginalization of nuisance parameters
  • Introduction to Bayesian model selection
  • Introduction to Bayesian decision theory.

See the course web page for more information.

Course Data
Type of schedule: 
Travel friendly schedule
Credits (ECTS):