Simulation / Edition 5

by Sheldon M. Ross

Overview

The 5^th edition of Ross's Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes.

This latest edition features all-new material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5^th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables.

By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, Ross's Simulation, 5^th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.

Product Details

ISBN-13:	9780124158252
Publisher:	Elsevier Science
Publication date:	10/22/2012
Pages:	328
Product dimensions:	6.10(w) x 9.10(h) x 0.90(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

Elements of Probability.

Random Numbers.

Generating Discrete Random Variables.

Generating Continuous Random Variables.

The Discrete Event Simulation Approach.

Statistical Analysis of Simulated Data.

Variance Reduction Techniques.

Statistical Validation Techniques.

Markov Chain Monte Carlo Methods.

Some Additional Topics.

qu:"...quite useful as a reference for the applied statistician."

source:—TECHNOMETRICS

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