00000cam a22000004i 4500 UP-99796217610966875 Buklod 20130924123657.0 a r |||| u| ta 120904s2013 xx a rb |001 0|eng d 9780124158252 (hardback) (iLib)UPD-00205806242 DLSCI pcc 519.2 23 QA 273 R82 2013 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. Probabilities. Computer simulation. UPD DNIP QA 273 R82 2013 UPD DSCI QA 273 R82 2013 Book