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Lecture Bayesian Networks Winter Term 2016/2017

You can inspect the results of your exam on Monday, 6th of February from 1pm to 3pm in room G29-015.



General Information

This page contains information about the lecture "Bayesian Networks" (in German "Bayes-Netze") that is held in winter term 2016/2017 by Prof. Dr. Rudolf Kruse. This page is updated during the course.


  • Representation of uncertain information
  • Bayesian networks
  • Markov networks
  • Evidence propagation in probabilistic networks
  • Learning of probabilistic networks
  • Revision of probabilistic networks
  • Decision Graphs
  • Handling imprecise data and imprecise probabilities
  • Applications
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Schedule and Rooms


Every student who wants to participate in the exercise must register her-/himself via the FIN Registration Service for the exercise. If you have any trouble with verifying the SSL certificate Jens Elkner could help you. While doing the registration, we kindly ask you to give an e-mail address of which incoming e-mail you check regularly.

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If you have questions regarding the lecture or exercise, please contact (via e-mail if possible) one of the persons named below.

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Conditions for Certificates (Scheine) and Exams

Certificate (Übungsschein): There are assignment sheets published every week. Assignments the solutions of which you want to present in the next exercise lecture have to be ticked beforehand on a votation sheet that is handed our prior to every exercise lecture. If ticked, you may be asked to present your solution in front of class. The solutions need not necessarily be completely correct, however, it should become obvious that you treated the assignment thoroughly. You are granted the certificate (Schein), if (and only if) you

  • ticked at least two thirds of the assignments,
  • presented at least two times a solution during the exercise, and
  • pass the exam

Exam: If you intend to finish the course with an exam, your are required to meet the certificate conditions. There will be a written exam after the curse. You can use your own not graphical and not programmable calculator.

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You should have background knowledge on fundamentals of computer science such as algorithms, data structures etc. Also, insights into probability theory are highly recommended.

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Note that the script may be subject to change (which will be stated in the news section above) during the course, i.e. page numbers may change.

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Assignment Sheets

The assignment sheets will be published weekly at this location.

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Additional Material

Feel free to check out the following supplementary material that augment the lecture and exercise.

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Here you find links to programs with for learning and using Bayesian networks.

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  • Computational Intelligence - A Methodological Introduction
    Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M.
    Springer-Verlag London 2013, 2016
  • Graphical Models - Representations for Learning, Reasoning and Data Mining, 2nd Edition.
    C. Borgelt, M. Steinbrecher und R. Kruse.
    J. Wiley & Sons, Chichester, United Kingdom 2009
  • An Introduction to Bayesian Networks.
    F.V. Jensen.
    UCL Press, London, United Kingdom 1996
  • Expert Systems and Probabilistic Network Models.
    E. Castillo, J.M. Gutierrez, and A.S. Hadi.
    Springer, New York, NY, USA 1997
  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
    J. Pearl.
    Morgan Kaufmann, San Mateo, CA, USA 1988 (2nd edition 1992)
  • Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
    Uffe B. Kjærulff, Anders L. Madsen
    Springer Science+Business Media New York 2013
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Page last modified on February 06, 2017, at 12:41 PM by pheld