Keshan-disease
发表于 2025-3-25 04:20:40
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生命层
发表于 2025-3-25 07:52:33
Stefan Schaltegger,Roger Burritt proportion of its running time executing searches for new space and creating and maintaining order among the myriad of entity records and event notices it generates as simulated time evolves. In spite of their relative importance, current PC workstation environments, with their substantial memories
EXALT
发表于 2025-3-25 14:40:16
Yoshihiro Ito,Hiroyuki Yagi,Akira Omorindent observations that serve as the raw material for estimating the true values of one or more long-run performance measures, hereafter called .. Two phenomena, . and ., influence how well an estimate approximates the true value of a parameter. Random input induces sampling error, and the dependenc
千篇一律
发表于 2025-3-25 18:03:18
Stefan Schaltegger,Roger Burritt delay time, queue length, and resource utilization. Sample averages summarize these data and usually become the focal point of reports and presentations to management. Since they merely approximate corresponding long-run averages that would be observed if the simulation were run for an infinite rat
Aesthete
发表于 2025-3-25 23:37:31
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compassion
发表于 2025-3-26 01:59:37
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粗野
发表于 2025-3-26 05:43:33
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macrophage
发表于 2025-3-26 11:07:13
Sampling from Probability Distributions,stributions. Often, this incorporation is relatively seamless, requiring the user merely to pull down a menu of options, select a distribution, and specify its parameters. This major convenience relieves the user of the need to write her or his code to effect sampling.
可耕种
发表于 2025-3-26 12:56:02
Pseudorandom Number Generation,e of i.i.d. random samples from .(0, 1) as input. To meet this need, every discrete-event simulation programming language provides a . that produces a sequence of nonnegative integers .., ..,... with integer upper bound . > .. ∀. and then uses .., ..,..., where .. := ../., as an approximation to an i.i.d. sequence from .(0, 1).
LEERY
发表于 2025-3-26 19:54:56
Preparing the Input,ulation models and executable programs, but only after we have identified all sources of stochastic variation, specified sampling distributions that characterize each source, and assign numerical values to the parameters of the distributions. This chapter addresses these issues.