000			01676nam a2200301 a 4500
999			_c23166 _d23166
001			BD-DhNSU-23166
003			BD-DhNSU
005			20211018153405.0
008			211018s2013 ukna|||g |||| 001 0|eng d
020			_a9780124158252
040			_aDLC _cBD-DhNSU _dBD-DhNSU
041			_aeng
050	0	0	_aQA273 _b.R67 2013
100	1		_aRoss, Sheldon M. _923559
245	0	0	_aSimulation / _cSheldon M. Ross
250			_a5th ed.
260			_aAmsterdam : _bElsevier Academic Press, _cc2013.
300			_axii, 310 p. : _bill. ; _c22+ cm.
504			_aIncludes bibliographical references and 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".
526	0		_aMathematics, Physics & Statistics
526	0		_aComputer Science & Engineering
590			_aDilruba Rahman
650		0	_aRandom variables _923554
650		4	_aProbabilities _91197
650		4	_aComputer simulation _923560
942			_2lcc _cBK