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184.236 Probabilistic Reasoning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VU, 2.0h, 3.0EC, to be held in blocked form


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to delineate the main application areas, formalisms, and methodologies for probabilistic modeling and reasoning in artificial intelligence. 

Subject of course

The course gives an overview on the area of probabilistic modeling and reasoning in artificial intelligence. Some planned topics are summarized as follows: basics of probability theory; Bayesian networks; probabilistic logic; nonmonotonic probabilistic inference; probabilistic logic programming; decision theory; planning under uncertainty in Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs); game theory.

Teaching methods

Solving exercise problems.


Mode of examination


Additional information

The course will be kept as a block (6 x 180 min during two weeks in May/June). It will be given in English. Further details on this course will be posted soon on this website.



Examination modalities

Oral examination at the end of the course. 

Course registration

Begin End Deregistration end
01.02.2023 00:00


Study CodeObligationSemesterPrecon.Info
066 011 Double degree programme "Computational Logic (Erasmus-Mundus)" Not specified


Previous knowledge

Elementary knowledge in logic.