02264cam a2200313 i 4500 17451335 OSt 20150408114201.0 120904s2013 ne a b 001 0 eng 2012027466 9780124158252 (hardback) DLC eng DLC rda DLC pcc QA273 .R82 2013 519.2 23 RSS Ross, Sheldon M. Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California. Fifth edition. xii, 310 pages : illustrations ; 24 cm Includes bibliographical references and index. Machine generated contents note: Preface; Introduction; Elements of Probability; Random Numbers; Generating Discrete Random Variables; Generating Continuous Random Variables; The Discrete Event Simulation Approach; Statistical Analysis of Simulated Data; Variance Reduction Techniques; Statistical Validation Techniques; Markov Chain Monte Carlo Methods; Some Additional Topics; Exercises; References; Index. "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"-- Random variables. 14356 Probabilities. Computer simulation. 7 cbc orignew 1 ecip 20 y-gencatlg ddc DVD 32986 267486 0 2014-01-07 0 0 519.2 RSS 2014-01-07 CL CL 2014-01-07 00:00:00 BOOK ddc 0