Сравнение методов

	Латинское гиперкубическое проектирование	Бутстреп-симуляция
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1979	1979
Автор метода	—	Bradley Efron
Тип	Stratified space-filling sampling design	Simulation-based nonparametric inference
Основополагающий источник	McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.
Другие названия	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)
Связанные	4	5
Сводка	Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.