Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Tuesday, November 19, 2019
Lecture K1: Variance Reduction Techniques, Part 1 (2019-11-19) – CRN and Control Variates
This lecture introduces the use of Variance Reduction Techniques (VRT), which combine tools from statistical experiment design with the idiosyncrasies of stochastic simulation studies to reduce the number of replications needed to make inferences by controlling sources of variance. This lecture primarily focuses on Common Random Numbers (CRN) and Control Variates.
Labels:
podcast
Location:
Tempe, AZ, USA
Subscribe to:
Post Comments (Atom)
Popular Posts
-
This lecture introduces students to IEE 475 (Simulating Stochastic Systems), a required course for Industrial Engineering majors that covers...
-
In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms,...
-
This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discret...
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (...
-
During this lecture, we review the topics covered up to this point in the course as preparation for the upcoming midterm exam. Students are ...
-
This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other...
-
In this lecture, we review statistical fundamentals – such as the origins of the t-test, the meaning of type-I and type-II error (and alter...
-
In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling...
-
In this lecture, we introduce Industrial and Systems Engineering as a blend of science and engineering that necessitates model building. We ...
No comments:
Post a Comment