Simulation
6th Edition
Published on 14. June 2022
336 pages
978-0-323-89961-1 (ISBN)
Description
Simulation, Sixth Edition 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 will learn to apply the results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions. 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, this book presents the statistics needed to analyze simulated data and validate simulation models.
- Includes updated content throughout
- Offers a wealth of practice exercises as well as applied use of free software package R
- Features the author's well-known, award-winning and accessible approach to complex information
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Person
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.
Content
1. Introduction2. Elements of Probability3. Random Numbers4. Generating Discrete Random Variables5. Generating Continuous Random Variables6. The Multivariate Normal Distribution and Copulas7. The Discrete Event Simulation Approach8. Statistical Analysis of Simulated Data9. Variance Reduction Techniques10. Additional Variance Reduction Techniques11. Statistical Validation Techniques12. Markov Chain Monte Carlo Methods
