Simulation / Sheldon M. Ross.
Material type: TextPublication details: San Diego : Academic Press / Elsevier , 2013.Edition: 5th edDescription: xii, 310 p. : ill. ; 24 cmContent type:- text
- unmediated
- volume
- 0124158250 (hardback)
- 9780124158252 (hardback)
- 519.2 22 ROS
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Book - Borrowing | Central Library First floor | Baccah | 519.2 ROS (Browse shelf(Opens below)) | 21759 | Available | 000030751 |
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Index : p. 303-310.
Includes bibliographical references.
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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