00000cam a22000003a 4500 UP-1685594773861970157 Buklod 20140902084512.0 g||| | || || ta 140902s2013 xx d r |||| u| 9780124158252 (hbk.) (iLib)UPBAG-00014703411 QA 273 R67 2013 Ross, Sheldon M. Simulation Sheldon M. Ross. 5th ed. San Diego, Calif. Academic Press c2013. xii, 310 p. ill. 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"--Provided by publisher. Random variables. Probabilities. Computer simulation. UPBAG UPBAG-MAIN QA 273 R67 2013 Book