In this lecture, we continue our discussion of input modeling in depth. We start with a more detailed example of how data collection can guide the choice of the structural features of a system. We then move to the point in the process when the structure of the model is set but the input models have to be chosen based on collected data. We cover methods for generating histograms and matching those histograms to common distributions (both discrete and continuous). We stop just before discussing Q-Q plots and P-P plots, which we will pick up next time along with discussing how to parameterize these chosen distributions.
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Tuesday, October 19, 2021
Lecture G2 (2021-10-19): Input Modeling, Part 2
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