In this lecture, we wrap up our discussion of Variance Reduction Techniques. We introduced Common Random Numbers (CRNs) last time, which we review in this lecture. We then introduce Control Variates (CVs), Antithetic Variates (AVs), and Importance Sampling. These four methods are all examples of amplifying signals in a statistical experiment either by manipulating the simulation execution or using information about known sources of variance to increase statistical power.
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...
-
This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other...
-
In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (...
-
In this lecture, we first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lectur...
-
In this lecture, we introduce Industrial and Systems Engineering as a blend of science and engineering that necessitates model building. We ...
-
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 basic probability fundamentals (measure spaces, probability measures, random variables, probability density funct...
No comments:
Post a Comment