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Elements of Probability
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
Ross's Simulation, Fourth Edition introduces 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 text explains 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. It presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.
- More focus on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis
- A chapter on Markov chain monte carlo methods with many examples
- Unique material on the alias method for generating discrete random variables
Senior/graduate level students taking a course in Simulation, found in many different departments, including: Computer Science, Industrial Engineering, Operations Research, Statistics, Mathematics, Electrical Engineering, and Quantitative Business Analysis
- No. of pages:
- © Academic Press 2006
- 1st August 2006
- Academic Press
- eBook ISBN:
"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
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.
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA
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