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
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, email@example.com
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||final version|
|2||Probabilistic Reasoning||final version|
|4||Bayes Networks Basics||final version|
|5||Separation in Graphs||final version|
|6||Evidence Propagation in Trees||final version|
|7||Clique Tree Representation||final version|
|8||Propagation - Real world applications||final version|
|9||Parameter Learning||final version|
|10||Structure Learning||final version|
|12||Causal Networks||final version|
|13||Decision graphs and Influence Diagrams||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 www.hugin.com
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