02119cam a22003135a 4500
17451335
20150819141932.0
120904s2013 caua frb 001 0 eng d
0124158250 (hardback)
9780124158252 (hardback)
DLC
eng
DLC
EG-ScBUE
519.2
22
ROS
Ross, Sheldon M.
7348
Simulation /
Sheldon M. Ross.
5th ed.
San Diego :
Academic Press / Elsevier ,
2013.
xii, 310 p. :
ill. ;
24 cm.
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"--
Random variables.
BUEsh
18426
Probabilities.
BUEsh
3494
Computer simulation.
BUEsh
BUEsh
COMSCI
August2015
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