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Lecture D1 (2020-09-17): Probability and Random Variables
IEE 475: Simulating Stochastic Systems
English - September 17, 2020 23:34Courses 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 C2 (2020-09-15): Beyond DES Simulation – SDM, ABM, and NetLogo (plus post-Lab3 discussion)
Next Episode: Lecture D2 (2020-09-22): Probabilistic Models
In this lecture, we introduce basic concepts from probability theory that will be useful as we move toward input modeling for Discrete Event System simulation modeling. Our introduction starts with a brief acknowledgment of measure theory and then a definition of random variables, sample spaces, events, and probability measures. We cover the discrete random variable, the continuous random variable, and the related probability mass and probability density functions. We pivot to discuss cumulative distribution functions and several applications of moments (expected value, mean, variance, standard deviation, etc.). Throughout the lecture, we use the analogy of probability as a kind of weight of a set of mutually exclusive outcomes.