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Lecture G3 (2020-10-22): Input Modeling, Part 3
IEE 475: Simulating Stochastic Systems
English - October 22, 2020 20:15Courses Education simulation stochastic des dess discrete event system industrial engineering modeling Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
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).