We start the lecture covering some discrete random variables that we did not get to during Lecture D2. We also introduce the Poisson process and how it relates to the Poisson and exponential random variables. We then pivot to discussing pseudo-random number generators (PRNGs), including their required as well as desired properties and statistical tests to test for independence and uniformity. We will continue the discussion of statistical tests for independence at the start of next lecture (Lecture E2).
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Thursday, September 23, 2021
Lecture E1 (2021-09-23): Random-Number Generation
Subscribe to:
Post Comments (Atom)
Popular Posts
-
In this lecture, we go over course policies for the Fall 2022 session of IEE 475.
-
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 introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
This lecture section is a cumulative review of material from the semester and is meant to serve as a study guide for students preparing for ...
-
Today's lecture covers the basics of probability (including introduction to measure spaces) and random variables. We also go over some r...
-
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 continue to discuss hypothesis testing -- introducing parametric, non-parametric, exact, and non-exact tests and reviewi...
-
This lecture continues to discuss issues related to estimating absolute performance from transient and steady-state simulations (of terminat...
-
In this lecture, we review fundamentals of Discrete Event System (DES) simulation (e.g., entities, resources, activities, processes, delays,...
-
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