![IEE 475: Simulating Stochastic Systems artwork](https://is4-ssl.mzstatic.com/image/thumb/Features/v4/5f/88/96/5f8896e9-9be5-529a-af14-cc807367568a/mza_1382744875394257421.png/100x100bb.jpg)
Lecture D2 (2020-09-22): Probabilistic Models
IEE 475: Simulating Stochastic Systems
English - September 22, 2020 22:58Courses 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 D1 (2020-09-17): Probability and Random Variables
Next Episode: Lecture E1 (2020-09-24): Random Number Generation
In this lecture, we review the motivations for stochastic modeling in discrete event system simulation. We also review the basics of probability theory (specifically probability spaces, random variables, probability density functions, probability mass functions, cumulative distribution functions, and moments including expected value (first moment/mean) and variance). We then describe several popular continuous and discrete random variables used in input modeling for stochastic simulation.