02348cam a2200349 i 4500 17451335 EG-ScBUE 20241109110913.0 120904s2013 caua f b 001 0 eng d 0124158250 (hardback) 9780124158252 (hardback) DLC eng rda DLC EG-ScBUE 519.2 22 ROS Ross, Sheldon M., author. 7348 Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California. Fifth edition. San Diego, CA : Academic Press / Elsevier, 2013. xii, 310 pages : illustrations ; 24 cm rdacontent text txt rdamedia unmediated n rdacarrier volume nc 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. BUEsh 18426 Probabilities. BUEsh 3494 Computer simulation. BUEsh BUEsh COMSCI August2015 Reading book ddc BB 20521 20493 0 0 ddc 0 0 Baccah MAIN MAIN 1ST 2015-08-16 purchase 425.00 21759 1 519.2 ROS 000030751 2025-07-15 00:00:00 2015-11-10 531.00 2015-08-16 BB