Vertaile menetelmiä

	Latin Hypercube Sampling	MONTE-CARLO-SIMULATION
Tieteenala	Simulointi	Päätöksenteko
Menetelmäperhe	Process / pipeline	MCDM
Syntyvuosi	1979	1949
Kehittäjä	—	Metropolis, N., Ulam, S.
Tyyppi	Stratified space-filling sampling design	Robustness wrapper — Monte Carlo uncertainty propagation
Alkuperäislähde	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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Rinnakkaisnimet	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design	—
Liittyvät	4	0
Tiivistelmä	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.