Bayesian Learning

The course introduces basic concepts, ideas and methods of Bayesian inference. It is coordinated with a master's course in the programme 'Statistics and Machine Learning' at LiU, and the focus with be 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 and is given every year in the second part of the spring semester. See the course web page for more details.

Contents
  • 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
University: 
Linköping
Type of schedule: 
Regular schedule
Level: 
Master
PhD
Credits (ECTS): 
6.00
Offered: 
2017:1