In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling. In particular, we start with a review of the two most important properties of a pseudo-random number generator (PRNG), uniformity and independence, and discuss statistically rigorous methods for testing for these two properties. For uniformity, we focus on a Chi-square/Chi-squared test for larger numbers of samples and a Kolmogorov–Smirnov (KS) test for smaller numbers of samples. For independence, we discuss autocorrelation tests and runs test, and then we demonstrate a runs above-and-below-the-mean test. We then shift to discussing inverse-transform sampling for continuous random variates and discrete random variates and how the resulting random-variate generators might be implemented in a tool like Rockwell Automation's Arena.
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Tuesday, October 1, 2024
Lecture E2 (2024-10-01): Random-Variate Generation
Subscribe to:
Post Comments (Atom)
Popular Posts
-
In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing...
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
This lecture covers Variance Reduction Techniques (VRT) for stochastic simulation, covering: Common Random Numbers (CRNs), Control Variates ...
-
In this lecture, we continue to discuss hypothesis testing -- introducing parametric, non-parametric, exact, and non-exact tests and reviewi...
-
In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last tim...
-
In this lecture, we wrap up the course content in IEE 475. We first do a quick overview of the four variance reduction techniques (VRT's...
-
In this lecture, we go over course policies for the Fall 2022 session of IEE 475.
-
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 fundamentals of Discrete Event System (DES) simulation (e.g., entities, resources, activities, processes, delays,...
-
In this lecture, we continue our discussion of statistically rigorous methods for input modeling in simulation of stochastic systems. We fir...
No comments:
Post a Comment