In this lecture, we continue our discussion of statistically rigorous methods for input modeling in simulation of stochastic systems. We first cover the basics of hypothesis testing, including a review of type-I error (alpha), p-values, and how they relate to critical values for goodness-of-fit tests (like Chi-squared and KS). We then review Q-Q and P-P probability plots to identify candidate families for input models from collected data. Then we discuss how maximum likelihood estimation (MLE) provides a bridge from summary statistics to mathematically justifiable choices for parameter values of the distributions we have chosen. Next time, we will discuss Chi-square testing and KS testing as applied to general probability distributions (i.e., not just as tests for uniformity).
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Thursday, October 22, 2020
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