Bayes Networks

General Information

This page contains information about the lecture "Bayes Networks" (in German "Bayes-Netze") that is held in winter term 2017/2018 by Prof. Dr. Rudolf Kruse. This page is updated during the course.


There won't be a lecture on the 11th of January! Please use the time for exam preparation ;)

Don't forget to register for the exam! Registration should be open till 15th January.


  • 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

Schedule and Rooms

Lecture Thursday 11.15-12.45 G29-307 12.10.2017 english
Exercise Tuesday 09.15-10.45 G29-K059 17.10.2017 english
Exercise Wednesday 13.15-14.45 G29-E037 18.10.2017 english

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.


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

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.


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


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.

Lecture 00 - Introduction

Lecture 01 - Rule-Based Systems

Lecture 02 - Probability Foundations

Lecture 03 - Decomposition

Lecture 04 - Separation Concepts

Lecture 05 - Probabilistic Graphical Models

Lecture 06 - Inference in Belief Trees

Lecture 07 - Clique Tree Representations

Lecture 08 - Propagation in Clique Trees

Lecture 09 - Manual Building of Bayes Networks

Lecture 10 - Building Bayes Networks: Parameter Learning

Lecture 11 - Building Bayes Networks: Structure Learning

Lecture 12 - Revision of Probabilistic Graphical Models

Lecture 13 - Decision Graphs - Influence Diagrams

Lecture 14 - Alternative Concepts

Assignment Sheets

The assignment sheets will be published weekly at this location.

Exercise Sheet 1 - Due to 17./18.10.2017

Exercise Sheet 2 - Due to 24./25.10.2017

Exercise Sheet 3 - Due to 01./07.11.2017

Exercise Sheet 4 - Due to 07./08.11.2017

Exercise Sheet 5 - Due to 21./22.11.2017

Exercise Sheet 6 - Due to 23.11. during the lecture

Exercise Sheet 7 - Due to 12./13.12.2017

Exercise Sheet 8 - Due to 19./20.12.2017

Exercise Sheet 9 - Due to 09./10.01.2018

Exercise Sheet 10 - Due to 16./17.01.2018

The final exercise classes will be on 23/24.01.2018. You can pose any questions on the upcoming exam and we will discuss some typical tasks.

Additional Material

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


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


  • 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


Last Modification: 16.01.2018 - Contact Person: Webmaster