Shakespeare and Bayes are in a boat, fishing. Bayes is trying to figure out which net to cast when Shakespeare says: "loopy or not loopy? that is the question".



Academic year 2019/2020:

This information is not yet complete.

An impression of the course can be found on last year's pages.


Learning goals (2019-2020): upon completing this course, the student

  1. recognises and understands the strengths and weaknesses of probabilistic graphical models in general and Bayesian networks in particular;
  2. understands the relation between probabilistic independence and the graphical representations thereof, and is able to draw conclusions from this relation;
  3. understands and is able to apply probabilistic inference in Bayesian networks;
  4. has knowledge and understanding of methods for constructing the Bayesian network graph for actual applications;
  5. has knowledge and understanding of various methods for quantifying Bayesian networks, including parameterized models (noisy-or), together with their benefits and limitations;
  6. understands and is able to apply techniques for evaluating the robustness and quality of a Bayesian network.