Applied Discrete Modelling
This page contains general information about the course "Applied Discrete Modelling", which usually takes place in the winter term. Information on the currently running course can be found here.
One target audience of this course are (consecutive) Master students, that have visited one or more simulation courses already. The course can broaden their knowledge on simulation methodology, modelling paradigms, and their application.
Students having no previous simulation knowledge but are interested in applying theoretical knowledge to novel and current application areas are invited to participate. There is no previous knowledge reauired beyond basic engineering math and programming skills. The course introduces and combines paradigms taken from simulation, modelling and pattern recognition.
The application examples touch on the broad areas of Medical Computer Science, Digital Engineering and Data and Knowledge Engineering. Therefore they provide the opportunity for different groups of students to integrate the course in their curriculum and draw benefit for their specific area of interest.
Goals of the Course
One goal of the course is to introduce students to the current research done in our group in the area of simulation methodology and its application. This will be a good preparation for a Master thesis in our group on these subjects. The theoretical contents of the course are the following:
- discrete time and continuous time Markov chains and their solution algorithms
- the method of supplementary variables
- proxel-based simulation and phase-type distributions
- hidden Markov models and hidden non-Markovian models
Semester Assignments are based on an application example from the current research fields of Digital Engineering, Data and Knowledge Engineering and Medical Computer Science. They are used to apply the knowledge and implement the algorithms taught throughout the semester. Students will see the different levels of applicability of the methods and understand their features and shortcomings. The improvements and new developments in current research will be motivated and demonstrated.