In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simulation models of systems. Those input models must be chosen to match statistical distributions in data. Over this unit, we cover collection of data for this process, choice of probabilistic families to fit to these data, and then optimized parameter choice within those families and evaluation of fit with goodness of fit. In this lecture, we discuss issues related to data collection.
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Friday, October 14, 2022
Lecture G1 (2022-10-13): Input Modeling, Part 1 (Data Collection)
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