
After successful completion of this part, the student is able to:
 analyse a practical problem in which uncertainty plays a role, and to design a conceptual discreteevent simulation model for it;
 implement a discreteevent simulation model by writing correct computer code;
 interpret the outcomes of the computer simulation program using statistical analysis.


This course aims to give an introduction to the basics of stochastic (discrete event) simulation. In practice, most systems give rise to mathematical models that are too complicated for an exact mathematical analysis. Making some simplifying assumptions may make our model (numerically) tractable but also give less applicable results. In these situations we can decide to do a simulation study of the original system. In a simulation study of a system we distinguish two parts namely the simulation of the system and the collecting and analysis of data with this simulation. A side goal is to improve programming skills in Python via the assignments.



 Assumed previous knowledgeStatistics (from module 5), Python (from module 7). 
Bachelor Applied Mathematics 
  Required materialsRecommended materialsHandouts 
 Instructional modesLecture
 Project supervised
 Project unsupervised

 TestsStochastic Simulation RemarkRepair is only possible for students who failed the first time. Repair grade is at most 5.5.


 