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Lecture G1 (2020-10-08): Input Modeling, Part 1
IEE 475: Simulating Stochastic Systems
English - October 09, 2020 00:00Courses Education simulation stochastic des dess discrete event system industrial engineering modeling Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Previous Episode: Lecture F (2020-10-01): Midterm Review
Next Episode: Lecture F2 (2020-10-13): Review Before Midterm Retake
In this lecture, we re-motivate the topic of Input Modeling in stochastic simulation. Input modeling is the process of choosing probabilistic models to represent realistic variation that is statistically similar to measured data even though the probabilistic models leave out the real-world details underlying that variation. This lecture focus primarily on issues relating to collecting data (when to use old data, issues related to data censoring, issues related to correlated variables, issues related to multi-modal distributions, etc.).