MARC Record from Library of Congress

LEADER: 02186cam a2200313 i 4500
001 2012027466
003 DLC
005 20130919075354.0
008 120904s2013 ne a b 001 0 eng
010 $a 2012027466
020 $a9780124158252 (hardback)
040 $aDLC$beng$cDLC$erda$dDLC
042 $apcc
050 00 $aQA273$b.R82 2013
082 00 $a519.2$223
100 1 $aRoss, Sheldon M.
245 10 $aSimulation /$cSheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.
250 $aFifth edition.
264 1 $aAmsterdam :$bAcademic Press,$c2013.
300 $axii, 310 pages :$billustrations ;$c24 cm
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
520 $a"In formulating a stochastic model to describe a real phenomenon, it used to be that one compromised between choosing a model that is a realistic replica of the actual situation and choosing one whose mathematical analysis is tractable. That is, there did not seem to be any payoff in choosing a model that faithfully conformed to the phenomenon under study if it were not possible to mathematically analyze that model. Similar considerations have led to the concentration on asymptotic or steady-state results as opposed to the more useful ones on transient time. However, the relatively recent advent of fast and inexpensive computational power has opened up another approach--namely, to try to model the phenomenon as faithfully as possible and then to rely on a simulation study to analyze it"--$cProvided by publisher.
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.
650 0 $aRandom variables.
650 0 $aProbabilities.
650 0 $aComputer simulation.