Bayesian Statistics I

This masters course introduces basic concepts, ideas and methods of Bayesian inference. It is given in the late fall at Stockholm University as part of the masters programme and in the late spring at Linköping University (then under the name Bayesian Learning) as part of the master's course in the programme 'Statistics and Machine Learning'. The focus is on models and methods in the field of machine learning, but most of the course content is of general statistical interest.
The course will be in English.

PhD students are welcome to take the course. The examination for PhD students consists of:

  • reports on the four computer labs
  • a project report where Bayesian methods and thinking are used to solve a problem in your own research area (or some other data analysis project, if you don't have a project yet).
  • 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 and variational inference.
  • 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: 
Regular schedule
Credits (ECTS):