Scripting on this page enhances content navigation, but does not change the content in any way. LHC is a re-scaling function in the domain of a random uniform variate so to have a better dispersion of the input numbers used to generate the pdf deviates. Use Latin Hypercube sampling when you are concerned primarily with the accuracy of the simulation statistics. After searching fow a while, I finally found a paper describing the LHC sampling (Swiler and Wyss 2004). (Compared to most simulation results, this extra overhead is minor.) The added expense of this method is the extra memory required to track which segments have been sampled while the simulation runs. Latin Hypercube sampling requires fewer trials to achieve the same level of statistical accuracy as Monte Carlo sampling. Latin Hypercube sampling is generally more precise when calculating simulation statistics than is conventional Monte Carlo sampling, because the entire range of the distribution is sampled more evenly and consistently. The Sample Size option (displayed when you select Run Preferences, then Sample), controls the number of segments in the sample. ![]() After has sampled each segment exactly once, the process repeats until the simulation stops. ![]() This collection of values forms the Latin Hypercube sample. While a simulation runs, selects a random assumption value for each segment according to the segment’s probability distribution.
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