Advanced Bayesian Learning

This is an advanced course in Bayesian statistics for PhD students in statistics, computer science, the engineering sciences and other related fields.
The course is divided into five contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier.
The topics for the current year are (teacher in parenthesis):

1. Gaussian Processes (Mattias Villani, Statistics LiU)
2. Bayesian Networks (Jose M. Pena, Database and Information Techniques, LiU)
3. Approximate Methods (Mattias Villani, Statistics LiU)
4. Sequential Monte Carlo (Thomas Schön, Automatic Control, UU)
5. Bayesian Nonparametrics (Mattias Villani, Statistics LiU)

The course starts on March 26, and the schedule is travel-friendly. For more information and an almost complete schedule, see the course page.
GRAPES will refund travel and accomodation costs for PhD students at Swedish universities.

The prerequisite for this course is the introductory course Bayesian Learning (given annually at Statistics, LiU) or something equivalent.
If you are interested in the taking this course, please contact Anne Moe at for registration.

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