Simulation
4th Edition
Published on 1. August 2006
312 pages
978-0-08-051722-3 (ISBN)
Description
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
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Sheldon M. Ross
Simulation
Book
09/2006
4th Edition
Academic Press
€81.70
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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
Elements of ProbabilityRandom NumbersGenerating Discrete Random VariablesGenerating Continuous Random VariablesThe Discrete Event Simulation ApproachStatistical Analysis of Simulated DataVariance Reduction TechniquesStatistical Validation TechniquesMarkov Chain Monte Carlo MethodsSome Additional Topics
