Sample Size Requierement for Monte Carlo - simulations using Latin Hypercube Sampling
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چکیده
منابع مشابه
Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models
Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional pro...
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McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of Monte Carlo simulations. More recently Owen (1992a) and Tang (1993) generalized LHS using orthogonal arrays. In the Owen's class of generalized LHS, we de ne extended Latin hypercube sampling of strengthm (henceforth denoted as ELHS(m)), such that ELHS(1) reduces to LHS. We rst derive explicit f...
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Latin Hypercube Sampling (LHS) and Jittered Sampling (JS) both achieve better convergence than standard Monte Carlo Sampling (MCS) by using stratification to obtain a more uniform selection of samples, although LHS and JS use different stratification strategies. The “Koksma-Hlawka-like inequality” bounds the error in a computed mean in terms of the sample design’s discrepancy, which is a common...
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One problem in computing cloud microphysical processes in coarse-resolution numerical models is that many microphysical processes are nonlinear and small in scale. Consequently, there are inaccuracies if microphysics parameterizations are forced with grid box averages of model fields, such as liquid water content. Rather, the model needs to determine information about subgrid variability and in...
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