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Simulation and Statistical Analysis

Full course description

Section titled “Full course description”

Mathematical Simulation is concerned with the study of processes and systems. When modelling a process or system there is often an uncertainty factor present. Such uncertainty is often caused by the random nature of certain factors that affect the process/system. In order to properly analyse a model it is important to correctly model any uncertainty that is present. Once the right model is in place various scenarios can be simulated, using Monte Carlo simulation, to gain insight. The results of such analyses need to be properly interpreted and uncertainty has to be reduced. The modelling, implementation, analysis and technical aspects will all be discussed in this course. The emphasis will be on discrete even simulation. After completing this course the students will be familiar with the essentials of simulation, such as the model cycle, discrete event simulation, output analysis and experimental design. Students will be able to employ simulation as a tool for evaluation.

Prerequisites

Section titled “Prerequisites”

Probability & Statistics, MATLAB

Section titled “Recommended reading”

Object-Oriented Computer Simulation of discrete-event systems – Jerzy Tyszer, Design and Analysis of Experiments – Douglas C. Montgomery, Introduction to Probability Models – Sheldon M. Ross.

Files

Section titled “Files”

Exam materials from before 2014 should be considered inaccurate representations of the current exams. They may indeed contain useful practice exercises, however the structure and contents of the exams before 2014 may have changed significantly.

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