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Lecture Bayesian Networks Winter Term 2013/2014

Die Klausur ist fertig korrigiert. Die Klausureinsicht ist am Mittwoch, den 26.2. von 10-11 Uhr im Raum G29-015.



General Information

This page contains information about the lecture "Bayesian Networks" (in German "Bayes-Netze") that is held in winter term 2013/2014 by Prof. Dr. Rudolf Kruse. This page is updated during the course.


  • Modeling of uncertainty and vagueness in expert systems
  • Representation of uncertain information in probabilistic networks (Bayesian networks / Markov networks)
  • Evidence propagation in such networks
  • Quantitative and structural induction of probabilistic networks from data
  • Other uncertainty calculi (Dempster-Shafer)
  • Applications
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Schedule and Rooms

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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
  • passed a small colloquium (approx. 10 min) or a written test (if there are more than 20 students) after the course.

Exam: If you intend to finish the course with an exam, your are required to meet the certificate conditions.

Regarding the exam, please contact and negotiate a date and time with Prof. Kruse. The exam has to be announced to the Prüfungsamt two weeks prior to the exam date via this application form. The exam consists of a 20 to 25 minutes oral examination about the subjects presented during the course. Emphasis is put on understanding rather than formal details. The final marks will be in the following range: 1.0, 1.3, 1.7, ..., 3.7, 4.0, 5.0. If more than 20 students intend to do an exam, the oral exam may be converted into a written examination.

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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.

In this year we want to setup a mailing list. This list should give you the opportunity to exchange interesting solutions and also solve problems concerning the topic. Additionally I will send important information and the current Assignment Sheet via the list. If you like to join this list, please send an email to .

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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
    R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, G. Ruß, M. Steinbrecher.
    Vieweg+Teubner, Wiesbaden, Germany 2011
  • 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
  • Handbuch der künstlichen Intelligenz
    G. Görz, C.-R. Rollinger und J. Schneeberger (Hrsg.).
    Oldenbourg, München, 2000
    In particular: C. Borgelt, H. Timm und R. Kruse.
    Kapitel 9: Unsicheres und vages Wissen.
  • 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)
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Page last modified on October 28, 2015, at 10:29 AM by pheld