Bayes Networks

General Information

This page contains information about the lecture "Bayes Networks" (in German "Bayes-Netze") that is held in winter term 2019/2020 by Prof. Dr. Rudolf Kruse. This page is updated during the course.


Announcement

No Lecture on October 3rd due to public holiday.


Topics

  • 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

  WeekdayTimeRoomBegin  
Lecture Thursday 11.15-12.45 G29-307 10.10.2019 english
Exercise    Wednesday 13.15-14.45 G29-K059 16.10.2019 english
Exercise Wednesday 15.15-16.45 G29-E037 17.10.2019 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.

 


Lecturers

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.

 


Prerequisites

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

 


Slides

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.

 

# Topic    Files   Announcements and Changelog
0 Introduction PDF The lecture will start on October 10th, the first lecture will be held in G29-307
1 Rule-Based Systems  PDF  
 2 Probability Foundations    
3 Decomposition    
4 Separation Concepts    
5 Probabilistic Graphical Models    
6  Inference in Belief Trees    
7  Clique Tree Representations    
8  Propagation in Clique Trees    
9  Manual Building of Bayes Networks    
10  Building Bayes Networks: Parameter Learning    
11  Learning Decision Trees    
12  Building Bayes Networks: Structure Learning    
13  Revision    
14  Decision Graphs    
15  Hidden Markov Models    

 


Assignment Sheets

The assignment sheets will be published weekly at this location.

Due dates may be adjusted according to the lectures progress.

 

ID Topic       Due Date      Exercise Sheet
      Wednesday   
1  Combinatorics, Certainty Factors, Probabilities  16.10. PDF
2    23.10.  
3    30.10.  
4   06.11.  
5    13.11.  
6   20.11.  
7    27.11.  
8   04.12.  
9    11.12.  
10   18.12.  

 

 


Additional Material

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

 


Software

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

 


References

  • 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

 


Links

Last Modification: 06.09.2019 - Contact Person:

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