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010 a| 2012027466;

020 a| 9780124158252 (hardback);

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049 a| EREEa| NEHH;

050 00 a| QA273b| .R82 2013;

082 00 a| 519.22| 23;

100 1 a| Ross, Sheldon M.=| ^A128186;

245 10 a| Simulationh| [electronic resource] /c| Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.;

250 a| Fifth edition.;

260 a| Amsterdam :b| Academic Press,c| 2013.;

300 a| xii, 310 pages :b| illustrations ;c| 24 cm;

504 a| Includes bibliographical references and index.;

505 8 a| 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.;

506 a| Available only to authorized users.;

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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Random variables.=| ^A23247;

650 0 a| Probabilities.=| ^A14253;

650 0 a| Computer simulation.=| ^A72820;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from eBook - Mathematics 2012u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780124158252;

856 40 z| Full text available from Knovel, Academic, General Engineering & Project Administrationu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fapp.knovel.com%2Fweb%2Ftoc.v%2Fcid%3AkpSE000016%2FviewerType%3Atoc%2Froot_slug%3Asimulation-5th-edition%3Fkpromoter%3Dfederation;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=1044919;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4279207-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 1/4/2016x| EBOOKz| JERESOURCE;

Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.

Author/creator	Ross, Sheldon M.
Format	Electronic
Edition	Fifth edition.
Publication Info	Amsterdam : Academic Press, 2013.
Description	xii, 310 pages : illustrations ; 24 cm
Supplemental Content	Full text available from eBook - Mathematics 2012
Supplemental Content	Full text available from Knovel, Academic, General Engineering & Project Administration
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Random variables. Probabilities. Computer simulation.

Contents	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.
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2012027466
ISBN	9780124158252 (hardback)

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Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Online Content Full text available from eBook - Mathematics 2012 Full text available from Knovel, Academic, General Engineering & Project Administration Full text available from Ebook Central - Academic Complete	✔ Available

Simulation / Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California.

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