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Simulation /

"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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ross, Sheldon M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Academic Press, 2013.
Edición:Fifth edition.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Simulation /  |c Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California. 
250 |a Fifth edition. 
264 1 |a Amsterdam :  |b Academic Press,  |c 2013. 
300 |a 1 online resource (xii, 310 pages) :  |b illustrations 
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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. 
504 |a Includes bibliographical references and index. 
505 0 |a Elements 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. 
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546 |a English. 
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650 0 |a Random variables. 
650 0 |a Probabilities. 
650 0 |a Computer simulation. 
650 0 |a Digital computer simulation. 
650 6 |a Variables aléatoires. 
650 6 |a Probabilités. 
650 6 |a Simulation par ordinateur. 
650 7 |a probability.  |2 aat 
650 7 |a simulation.  |2 aat 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Digital computer simulation  |2 fast 
650 7 |a Computer simulation  |2 fast 
650 7 |a Probabilities  |2 fast 
650 7 |a Random variables  |2 fast 
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