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

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.

Reviews

"This textbook contains and describes all the tools one needs to plan and to carry out a simulation study as well as to analyze its results." --J.Wolters, zbMATH Open

"It presents the statistics needed to analyze simulated data and to validate the simulation model. In this edition, several new topics are included as well as a number of new exercises." --Vigirdas Mackevičius, zbMATH Open