Advanced Bayesian Learning, 8 credits

Submitted by matvil on November 27, 2019 - 17:55

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 four contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier. Students can pick and choose among the topics and will be given 2 credits for each selected topic.

1. Gaussian Processes with Applications
2. Bayesian Nonparametrics
3. Variational Inference
4. Bayesian Regularization and Variable Selection

Schedule and Location: The course will be give in the period April 16 - May 26 at Stockholm University (no hybrid). More information and schedule on the course page.

Prerequisites: The participants are expected to have taken at least an introductory course, for example the master's level course Bayesian Learning, or something equivalent.

Examination: each topic is examined by an individual report on a set of problems, typically involving a mix of mathematics and programming. There will be deadlines for each topic, but also an extra date after the summer for those who could complete some of the labs during the course.
Course Data
Type of schedule: 
Travel friendly schedule
University: 
Stockholm
Level: 
PhD
Credits (ECTS): 
8.00
Offered: 
2026:1