Bayes Networks

General Information

This page contains information about the lecture "Bayesian Networks". The course is held in winter term 2022/2023 by Prof. Dr. Rudolf Kruse.

Both lecture and tutorials will be given in English. The course is useful for computer scientists, mathematicians, and data engineers.

You should have background knowledge on fundamentals of mathematics and computer science. Also insights into probability theory are highly recommeded.



 The course starts on 12.10.2022



  • Representation of uncertain information
  • Bayesian Networks
  • Decomposition of spaces and separation of graphs
  • Evidence propagation in probabilistic networks
  • Learning of probabilistic networks
  • Revision of probabilistic networks
  • Decision Graphs and Influence Diagrams
  • Causal Networks



  WeekdayTime Begin  Room
Lectures  Wednesday 11:15-12:45   12.10.22  G29-336
Tutorials    Wednesday 13:15-14:45   12.10.22  G29-336



Lectures and Tutorials

Lectures: Prof. Dr. Rudolf Kruse,

You'll find the slides of the lectures below. Additionally we provide videos of the lectures on our OVGU Cloud.

Tutorials: Daniel Stelter,

All information about the tutorials (such as exercise sheets) are provided on our OVGU Cloud.


Conditions for  Exams

A new assignment sheet containing written and programming assignments is published every week. You must vote on for each assignment, which means that you are willing and able to explain and present the assignment and your solution proposal )which does not need to de completely correct) during the tutorial. However, you should be prepared thouroghly in order to present your solution,


The graded certificate for this course is issued to students who

  • regularly contribute well in the exercises,
  • submitted at least 2/3 of all written assignments
  • presented at least twice a solution to a written assignment during the exercise (this number is reduced in case not everybody can present twice due to number of exercises)
  • finally pass the exam after the course

The written exam is on February 1, 2023, 12-14 h



Note that the slides may be subject to improvement during the course.


# Topic    Files   Announcements and Changelog
 1  Rule Based Systems  PDF  final version
 2  Probabilistic Reasoning  PDF  final version
3  Decomposition PDF  final version
4  Bayes Networks Basics PDF  final version
5  Separation in Graphs PDF  final version
6 Evidence Propagation in Trees PDF  final version
7 Clique Tree Representation PDF  final version
8 Propagation - Real world applications PDF  final version
9 Parameter Learning PDF  final version
10 Structure Learning PDF  final version
11 Revision PDF  final version
12 Causal Networks PDF  final version
13 Decision graphs and Influence Diagrams PDF  final version




We recommend to use the HUGIN software for Bayesian Network and Inference Diagram technology. 

HUGIN LITE is a free version for download, see



LiteratureComputational Intelligence - A Methodological Introduction

Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M., Computational Intelligence - A methodological introduction, 3. Edition, Springer, New York, 2022


Last Modification: 15.11.2022 - Contact Person:

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