![]() ![]() ![]() Scripting on this page enhances content navigation, but does not change the content in any way. Use Latin Hypercube sampling when you are concerned primarily with the accuracy of the simulation statistics. Maximize the minimum distance between points and place the point in a randomized. ![]() (Compared to most simulation results, this extra overhead is minor.) Latin Hypercube sampling Center the points within the sampling intervals. 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. These methods propose to stratify sampling in presence of ancillary data. 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. Implementation of the conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) and the DLHS variant method (Minasny and McBratney, 2010). The Sample Size option (displayed when you select Run Preferences, then Sample), controls the number of segments in the sample. Roughly speaking, suppose we want to nd the mean of some known function G(y(x)) over X. Latin hypercube sampling is a statistical method for generating a sample of plausible collections of parameter values from a multidimensional distribution. After has sampled each segment exactly once, the process repeats until the simulation stops. Latin hypercube sampling and subsequently other authors (for example, Stein 1987 and Owen 1992) have explored their properties. Usage Latinhyper (parRange, num) Arguments Details In the latin hypercube sampling, the space for each parameter is subdivided into num equally-sized segments and one parameter value in each of the segments drawn randomly. This collection of values forms the Latin Hypercube sample. Latin Hypercube Sampling Description Generates random parameter sets using a latin hypercube sampling algorithm. While a simulation runs, selects a random assumption value for each segment according to the segment’s probability distribution. ![]()
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