MS&E 223 Simulation Peter J. Haas
Lecture Notes #1 Introduction to Simulation Spring Quarter 2009-10
Introduction to Simulation
1. What Is Simulation? (Law: Sections 1.1, 1.2, 1.9) Example: Consider a gambling game in which a fair coin is repeatedly flipp
MS&E 223 Simulation Brad Null
Lecture Notes #10 Steady-State Simulation Spring Quarter 2008-09
Steady-State Simulation
Reading: Law, Chapter 9, Handbook of Simulation, Chapter 15. We will discuss several techniques for obtaining point estimates and confid
MS&E 223 Simulation Brad Null
Lecture Notes #9 Quantile Estimation Spring Quarter 2008-09
Quantile Estimation
1. Quantiles (Definition and Simple Point Estimate) Quantiles form an important class of performance measures. The following examples show the ut
MS&E 223 Simulation Brad Null
Lecture Notes #8 Estimating Non-linear Functions of Means Spring Quarter 2008-09
Estimating Nonlinear Functions of Means
Back at the beginning of the course, we discussed how to obtain point estimates and confidence intervals
MS&E 223 Simulation Peter J. Haas
Lecture Notes #7 Event Lists Spring Quarter 2009-10
Event Lists
1. Event Lists Based on our algorithm for generating sample paths of a GSMP, it is clear that one way to implement the clock-setting mechanism is to maintain
MS&E 223 Simulation Peter J. Haas
Lecture Notes #6 Uniform Variate Generation Spring Quarter 2009-10
Uniform Variate Generation
Refs: Law Ch. 7; Simulation (Ed. Henderson et al.) Ch. 3.; Knuth, The Art of Computer
Programming, Vol. 2, Ch. 3.
We now discus
MS&E 223 Simulation Peter J. Haas
Lecture Notes #5 Generation Of Non-Uniform Random Numbers Spring Quarter 2009-10
Generation of Non-Uniform Random Numbers
Refs: Law, Ch. 8, and Devroye, Non-Uniform Random Variate Generation (watch for typos!) Problem: Gi
MS&E 223 Simulation Peter J. Haas
Lecture Notes #4 Input Distributions Spring Quarter 2009-10
Input Distributions
Ref: Chapter 6 in Law and Kelton To specify a simulation model for a discrete-event system, we need to define the distributions of the clock-
MS&E 223 Simulation Peter J. Haas
Lecture Notes #3 Generalized Semi-Markov Processes Spring Quarter 2009-10
Generalized Semi-Markov Processes (GSMP's)
Ref: Section 1.4 in Shedler or Section 4.1 in Haas 1. Motivation The Markov and semi-Markov models that
MS&E 223 Simulation Peter J. Haas
Lecture Notes #2 Stochastic Process Models for DESS Spring Quarter 2009-10
Some Stochastic Process Models for Discrete-Event Stochastic Systems
1. Discrete-Event Stochastic Systems Recall our previous definition of a disc
MS&E 223 Simulation Brad Null
Supplemental Notes Making Decisions via Simulation Spring Quarter 2008-09
Making Decisions via Simulation
Ref: Law, Chapter 10; Handbook of Simulation, Chapters 17-21; Haas, Sec. 6.3.6. We give an introduction into some metho