This page contains information about the lecture "Bayes Networks" (in German "Bayes-Netze") that is held in winter term 2018/2019 by Prof. Dr. Rudolf Kruse. This page is updated during the course.
No lecture on 29th November 2018!
- 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
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.
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 04 - Separation Concepts (updated definition of the minimal ancestral subgraph)
Lecture 06 - Inference in Belief Trees (corrected calculation of the pi component on slide 220 and 221)
The assignment sheets will be published weekly at this location.
Exercise Sheet 07 - Submission due to 4th/5th December (see "Illustration of Simple Tree Propagation" in the Additional Material section for help)
Exercise Sheet 08 - Submission due to 11th/12th December (see "Joint Tree Propagation" in the Additional Material section for help)
no Exercise in the week of 1st and 2nd January
Exercise Sheet 10 will be due to 8th/9th January
Exercise Exam Preparation Lesson will be on the 15th/16th January
no Exercise in the week of 22nd/23rd January
Feel free to check out the following supplementary material that augment the lecture and exercise.
- Data Mining with Graphical Models dmwgm.pdf
- Illustration of Simple Tree Propagation
- Illustration of Joint Tree Propagation
- Example Network used in the clique propagation lesson.
- Blood group determination of Danish Jersey cattle in the F-blood group system
- Introductory slides about belief functions (Source: Thierry Denoeux)
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
- Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Uffe B. Kjærulff, Anders L. Madsen
Springer Science+Business Media New York 2013