Simulation

"In formulating a stochastic model to describe a real phenomenon, it used to be that one compromised between choosing a model that is a realistic replica of the actual situation and choosing one whose mathematical analysis is tractable. That is, there did not seem to be any payoff in choosing a model that faithfully conformed to the phenomenon under study if it were not possible to mathematically analyze that model. Similar considerations have led to the concentration on asymptotic or steady-state results as opposed to the more useful ones on transient time. However, the relatively recent advent of fast and inexpensive computational power has opened up another approach--namely, to try to model the phenomenon as faithfully as possible and then to rely on a simulation study to analyze it"--

Autorentext
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.

Klappentext

Ross's Simulation, 5e, 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.

Zusammenfassung
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. This title presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.

Inhalt

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