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"Every model is an approximation. It is the data that is real." - Unknown.
"The only man I know who behaves sensibly is my tailor; he takes my measurements anew each time he sees me. The rest go on with their old measurements and expect me to fit them." - George Bernard Shaw
"I think chance is a more fundamental conception that causality; for whether in a concrete case, a cause-effect relation holds or not can only be judged by applying the laws of chance to the observation" – Max Born.
Lecture outline: How can the input to simulation models be modeled?
1) Collecting an input model
Collecting and representing data.
Evaluating time-dependence.
2) Testing an input model.
Heuristic graphics methods.
Statistical methods: Kolmogorov-Smirnov (K-S) test
Primary reference for this lecture:
“Discrete-event simulation – a first course”, by Leemis and Park; Chapter 9: “Input Modeling”.
“The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling” by Raj Jain; Chapter 25: “Analysis of Simulation Results”.
Secondary references for this lecture:
1. “Network Modeling and Simulation, A Practical Approach” by Mohsen Guizani et al.; Chapter 10, “Input Modeling and Output Analysis”.
2. "Handbook of Simulation", edited by J Banks, Part 2: Chapter 3, Input Data Analysis.
3. “Discrete-event simulation – a first course”, by Leemis and Park; Chapter 8: “Output Analysis”.
4. “Fundamentals of Performance Evaluation of Computer and Telecommunication System”, by Obaidat and Boudriga; Chapter 11: “Analysis of Simulation Results”.
5. "Handbook of Simulation", edited by J Banks, Part 2: Chapter 7, Simulation Output Analysis
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