Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.
By: Ross, Sheldon M
Publisher: Amsterdam: Academic Press, 2013Edition: Fifth edition. 5th edDescription: xii, 310 pages : illustrations ; 24 cmISBN: 9780124158252 (hardback)Subject(s): Random variables | Probabilities | Computer simulationDDC classification: 519.2 LOC classification: QA273 | .R82 2013Item type | Current location | Call number | Status | Notes | Date due | Barcode |
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Book | MILA University Central Library General Stacks | QA273 .R82 2013 (Browse shelf) | Available | SOBM | 0002310 |
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"--
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