MARC Record from marc_columbia

LEADER: 04173cam a2200661Ii 4500
001 13725583
005 20220816143058.0
006 m o d
007 cr |n|||||||||
008 121119s2013 ne a ob 001 0 eng d
010 $z 2012027466
035 $a(OCoLC)ocn818733978
035 $a(NNC)13725583
040 $aYDXCP$beng$erda$epn$cYDXCP$dOCLCO$dE7B$dOCLCQ$dCUV$dN$T$dCDX$dN$T$dOSU$dIDEBK$dOCLCQ$dOCLCF$dKNOVL$dZCU$dOCLCQ$dKNOVL$dOCLCQ$dVT2$dSTF$dOCLCQ$dBUF$dCOCUF$dCNNOR$dLOA$dK6U$dBRX$dU3W$dCEF$dOCLCQ$dTKN$dCNCEN$dWYU$dG3B$dLVT$dS8J$dS9I$dD6H$dUKCRE$dVLY$dUKAHL$dOCLCO$dOCL
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020 $a0124159710$q(electronic bk.)
020 $a9780124159716$q(electronic bk.)
020 $z9780124158252$q(hardback)
020 $z0124158250$q(hardback)
020 $a1283696134
020 $a9781283696135
035 $a(OCoLC)818733978$z(OCoLC)961848443$z(OCoLC)988674014$z(OCoLC)999536549$z(OCoLC)1042917526$z(OCoLC)1043715370$z(OCoLC)1055391849$z(OCoLC)1059248694$z(OCoLC)1081192954$z(OCoLC)1153542672$z(OCoLC)1162567077$z(OCoLC)1228579745$z(OCoLC)1290084404
050 4 $aQA273$b.R82 2013
072 7 $aMAT$x029000$2bisacsh
082 04 $a519.2$223
049 $aZCUA
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 $a1 online resource (xii, 310 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $adata file$2rda
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 0 $aElements of probability -- Random numbers -- Generating discrete random variables -- Generating continuous random variables -- The multivariate normal distribution and copulas -- The discrete event simulation approach -- Statistical analysis of simulated data -- Variance reduction techniques -- Additional variance reduction techniques -- Statistical validation techniques -- Markov chain Monte Carlo methods.
588 0 $aPrint version record.
546 $aEnglish.
650 0 $aRandom variables.
650 0 $aProbabilities.
650 0 $aComputer simulation.
650 0 $aDigital computer simulation.
650 6 $aVariables aléatoires.
650 6 $aProbabilités.
650 6 $aSimulation par ordinateur.
650 7 $aprobability.$2aat
650 7 $asimulation.$2aat
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aDigital computer simulation.$2fast$0(OCoLC)fst00893651
650 7 $aComputer simulation.$2fast$0(OCoLC)fst00872518
650 7 $aProbabilities.$2fast$0(OCoLC)fst01077737
650 7 $aRandom variables.$2fast$0(OCoLC)fst01089812
655 4 $aElectronic books.
776 08 $iPrint version:$aRoss, Sheldon M.$tSimulation.$bFifth edition$z9780124158252$w(DLC) 2012027466$w(OCoLC)741548323
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13725583$zACADEMIC - General Engineering & Project Administration
852 8 $blweb$hEBOOKS