Ross, Sheldon M.
Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California. - Fifth edition. 5th ed - Amsterdam: Academic Press, 2013. - 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"--
9780124158252 (hardback)
2012027466
Random variables.
Probabilities.
Computer simulation.
QA273 / .R82 2013
519.2
Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California. - Fifth edition. 5th ed - Amsterdam: Academic Press, 2013. - 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"--
9780124158252 (hardback)
2012027466
Random variables.
Probabilities.
Computer simulation.
QA273 / .R82 2013
519.2