Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.
Material type: TextPublisher: Amsterdam : Academic Press, 2013Edition: Fifth editionDescription: xii, 310 pages : illustrations ; 24 cmContent type:- text
- unmediated
- volume
- 9780124158252
- 519.2 23 ROS
- QA273 ROS
Item type | Current library | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|
Books | GSU Library Epoch General Stacks | QA273ROS (Browse shelf(Opens below)) | Available | 50000002030 |
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.
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