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- W1444950285 abstract "Publisher Summary This chapter focuses on digital-computer modeling and on the analysis of stochastic systems to estimate performance measures. Both dynamic-system simulation and Monte Carlo simulation of models having no time component are considered in the chapter. Simulation is a method of modeling and analyzing stochastic systems using digital computers. A simulation model is an expression of the logical relationships that cause the system to change from one state to another. Given a simulation model,five steps are required to perform a simulation experiment: (1) select a source of randomness; (2) obtain basic observations from the source; (3) transform the basic observations to input observations having known distributions; (4) transform the input observations, via the model, to output observations; and (5) calculate statistics from the output observations to estimate the performance measures. The chapter discusses the issues that an analyst faces in each step of a simulation experiment. Output analysis is concerned with estimating the sampling properties of the point estimator. The prototype problem considers estimating the standard error of the sample mean of stationary autocorrelated data, an estimate of the population mean. Variations include nonstationary data and/or other properties, such as variance, higher-order moments and cross moments, quantiles, and percentiles. The use of the estimate of standard error in a variety of analysis procedures, such as for confidence intervals and for ranking and selection, also falls within the topic of output analysis." @default.
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- W1444950285 date "1990-01-01" @default.
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- W1444950285 title "Chapter 7 Simulation experiments" @default.
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- W1444950285 doi "https://doi.org/10.1016/s0927-0507(05)80171-9" @default.
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