Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
IEE 475: Simulating Stochastic Systems
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67 episodes
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Podcast Overview
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
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🇺🇲
Publishing Since
9/20/2022
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Recent Episodes
December 2, 2025
Lecture M (2025-12-02): Final Exam Review
In this lecture, we prepare for the final exam and give a brief review of all topics from the course.
November 25, 2025
Lecture K2 (2025-11-25): Variance Reduction Techniques, Part 2 (Antithetic Variates and Importance Sampling)
In this lecture, we review four different Variance Reduction Techniques (VRT's). Namely, we discuss common random numbers (CRNs), control variates, antithetic variates (AVs), and importance sampling. Each one of these is a different approach to reducing the variance in the estimation of relative or absolute performance of a simulation model. Variance reduction is an alternative way to increase the power of a simulation that is hopefully less costly than increasing the number of replications. We wrap up with a summary of the four VRT's that we have discussed in the class – Common Random Numbers (CRN's), Antithetic Variates (AV's), Importance Sampling, and Control Variates.
November 21, 2025
Lecture K1 (2025-11-20): Variance Reduction Techniques, Part 1 (CRNs and Control Variates)
In this lecture, we start by reviewing approaches for absolute and relative performance estimation in stochastic simulation. This begins with a reminder of the use of confidence intervals for estimation of performance for a single simulation model. We then move to different ways to use confidence intervals on mean DIFFERENCES to compare two different simulation models. We then move to the ranking and selection problem for three or more different simulation models, which allows us to talk about analysis of variance (ANOVA) and post hoc tests (like the Tukey HSD or Fisher's LSD). After that review, we move on to introducing variance reduction techniques (VRTs) which reduce the size of confidence intervals by experimentally controlling/accounting for alternative sources of variance (and thus reducing the observed variance in response variables). We discuss Common Random Numbers (CRNs), which use a paired/blocked design to reduce the variance caused by different random-number streams, and then we introduce control variates (CVs), which allow for reducing the output variance of a measurement by leveraging information about other sources that may be corrupting the output.
67 total episodes available
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