000			02312cam a2200325 i 4500
999			_c1588 _d1588
001			17451335
003			OSt
005			20240607093105.0
008			120904s2013 ne a b 001 0 eng
010			_a 2012027466
020			_a9780124158252 (hardback)
040			_aDLC _beng _cDLC _erda _dDLC _dMIU
042			_apcc
050	0	0	_aQA273 _b.R82 2013
082	0	0	_a519.2 _223
100	1		_aRoss, Sheldon M.
245	1	0	_aSimulation / _cSheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.
250			_aFifth edition. _b5th ed
260			_aAmsterdam: _bAcademic Press, _c2013.
300			_axii, 310 pages : _billustrations ; _c24 cm
504			_aIncludes bibliographical references and index.
505	8		_aMachine 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.
520			_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"--
650		0	_aRandom variables.
650		0	_aProbabilities.
650		0	_aComputer simulation.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK