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Bayesian Networks


Admission requirements

The student should have taken a course in AI.


In 2012 Judea Pearl was given the Turing Award (seen as the Nobel prize in computer science) for his groundbreaking work on probabilistic and causal reasoning in intelligent systems. His work forms the core of this artificial intelligence (AI) course, which now, because of the Turing Award, is generally seen as one of the most important current topics of computer science. Handling uncertain knowledge has been one of the central problems of AI research during the past 30 years. In the 1970s and 1980s uncertainty was handled by means of formalisms that were linked to rule-based representation and reasoning methods. Since the 1990s probabilistic graphical models, in particular Bayesian networks, are seen as the primary formalisms to deal with uncertain knowledge. Various fundamental aspects of probabilistic graphical models, such as graphical representation, the logic of independence, reasoning and learning, are studied in this course, with some emphasis on Bayesian networks. In addition, building Bayesian networks for real-world applications will be covered and students will obtain some experience in how to build Bayesian networks for a problem domain in the practical and through small projects. Connections to cognitive science can be established in the associated seminar.

Course objectives

At the end of the Bayesian Network course, the student should be able to:

  • understand the probabilistic principles of reasoning under uncertainty

  • explain the differences between various graphical models in particular in terms of representation of independence information

  • have insight into algorithms for probabilistic reasoning in Bayesian networks

  • have insight into the pros and cons of learning models versus using expert knowledge

  • have some experience in exploiting software to solve problems involving uncertainty

  • in the seminar, student will learn how to interpreter the scientific literature on Bayesian networks and related probabilistic graphical models


The most recent timetable can be found at the LIACS website

Mode of instruction

  • lectures

  • seminar

  • tutorials

  • practical assignment/project

Assessment method

The final mark is composed of
(1) written exam (35%)
(2) practical assignment (30%)
(3) seminar and essay (35%)

Reading list

K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, Boca Raton, 2004 or 2010

Background literature:

  • R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J. Spiegelhalter, Probabilistic Networks and Expert Systems, Springer, New York, 1999

  • F.V. Jensen and T. Nielsen, Bayesian Networks and Decision Graphs, Springer, New York, 2007

  • D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, Cambridge, MA, 2009


You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

Contact information

Study coordinator Computer Science, Riet Derogee


Bayesian Networks