Simulation

by Sheldon M. Ross

Overview

Replete with easy-to-understand examples ranging from the prediction of home runs in baseball using an hierarchical

Bayesian statistics model to estimating the expected return at blackjack using control variables, this text functions as a complete

consideration of simulation. Sheldon Ross provides broad yet thorough coverage of the subject, presenting the development of

a simulation study to analyze models, and demonstrates that by using random variables and the concept of discrete events, it is

possible to generate the behavior of a stochastic model over time. Also discussed are questions concerning when to stop a

simulation, how much confidence can be placed in the results, and extensive new information on the presentation of the alias

method for generating discrete random variables material not found in any other text. Students, practitioners, and researchers

alike will find this text to have an important place in their research libraries.

Product Details

ISBN-13:	9780323857390
Publisher:	Elsevier Science
Publication date:	11/06/2022
Edition description:	6th ed.
Pages:	336
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.

Table of Contents

1. Introduction
2. Elements of Probability
3. Random Numbers
4. Generating Discrete Random Variables
5. Generating Continuous Random Variables
6. The Multivariate Normal Distribution and Copulas
7. The Discrete Event Simulation Approach
8. Statistical Analysis of Simulated Data
9. Variance Reduction Techniques
10. Additional Variance Reduction Techniques
11. Statistical Validation Techniques
12. Markov Chain Monte Carlo Methods

What People are Saying About This

From the Publisher

Applied examples illustrate the theory and motivate the student

Editorial Reviews

In the old days, says Ross (industrial engineering and operations research, U. of California-Berkeley), scientists formulating a stochastic model had to compromise between one that realistically portrayed the situation and one that could be comprehended mathematically. No more, he explains, now that fast and powerful computers are available cheap. He shows how to construct a model as faithful as possible to the phenomenon, then analyze it using a simulation study. He includes exercises for course study, but does not specify prerequisites. There's no mention of the dates of earlier editions. Annotation c. Book News, Inc., Portland, OR (booknews.com)

"His is a book that makes a very serious effort to go in the same direction as the changes in computing technology. To the best of my knowledge this is the only teaching book on simulation. It is outstanding because it is what it is and no other textbook out there does this job." —Kris Ostaszewski, Illinois State University

"Examples are infinitely more interesting than in almost any other book! Ross always explains clearly, I especially enjoy the exposition of the brand new sections." —Matt Carlton, Cal Polytechnic Institute

From the Publisher