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02070cam a2200277 i 4500 |
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20170321142429.0 |
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120904s2013 ne a b 001 0 eng |
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|a 9780124158252 (hardback)
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|a DLC
|b eng
|c DLC
|e rda
|d HR-ZaFER
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|a eng
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|a pcc
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|a QA273
|b .R82 2013
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|a 519.2
|2 23
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| 100 |
1 |
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|a Ross, Sheldon M.
|9 8706
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| 245 |
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|a Simulation /
|c Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.
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| 250 |
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|a 5 ed.
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| 260 |
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|a Amsterdam :
|b Elsevier,
|c 2013.
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| 300 |
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|a xii, 310 str. :
|b illustr. ;
|c 24 cm
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| 504 |
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|a Includes bibliographical references and index.
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| 505 |
8 |
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|a 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.
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| 520 |
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|a "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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| 650 |
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|a Random variables.
|9 38374
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| 650 |
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0 |
|a Probabilities.
|9 35656
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| 650 |
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|a Computer simulation.
|9 38375
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| 942 |
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|2 udc
|c K
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|c 47219
|d 47219
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