Fast Generation of Space-filling Latin Hypercube Sample Designs
نویسندگان
چکیده
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 metric of uniformity. However, even the “fast” formulas available for certain useful L2 norm discrepancies requireO ( NM )
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