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Simulation
This reference shows the reader 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 as well as presenting the statistics needed to analyze simulated data and the simulation model
Sheldon M. Ross (Author)
9780124158252, Elsevier Science
Hardback, published 7 December 2012
328 pages
22.9 x 15.1 x 2.4 cm, 0.54 kg
"I have always liked Ross’ books, as he is simultaneously mathematically rigorous and very interested in applications. The biggest strength I see is the rare combination of mathematical rigor and illustration of how the mathematical methodologies are applied in practice. Books with practical perspective are rarely this rigourous and mathematically detailed. I also like the variety of exercises, which are quite challenging and demanding excellence from students." --Prof. Krzysztof Ostaszewski, Illinois State University
The 5th 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 5th 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, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.
Chapter 1 - IntroductionChapter 2 - Elements of ProbabilityChapter 3 - Random NumbersChapter 4 - Generating Discrete Random VariablesChapter 5 - Generating Continuous Random VariablesChapter 6 - The Multivariate Normal Distribution and CopulasChapter 7 - The Discrete Event Simulation ApproachChapter 8 - Statistical Analysis of Simulated DataChapter 9 - Variance Reduction TechniquesChapter 10 - Additional Variance Reduction TechniquesChapter 11 - Statistical Validation TechniquesChapter 12 - Markov Chain Monte Carlo Methods
Subject Areas: Stochastics [PBWL], Probability & statistics [PBT]
