Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Hybride Volledig Factorieel Ontwerp× | Latin Hypercube Sampling× | |
|---|---|---|
| Vakgebied≠ | Experimenteel ontwerp | Simulatie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1980s–2000s (building on Fisher's 1935 factorial framework) | 1979 |
| Grondlegger≠ | Derived from classical factorial design theory (Fisher, 1935); hybrid extensions developed across engineering literature from the 1980s onward | — |
| Type≠ | Experimental design strategy | Stratified space-filling sampling design |
| Oorspronkelijke bron≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | 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. DOI ↗ |
| Aliassen | hybrid factorial design, mixed full factorial design, combined factorial design, HFFD | LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | Hybrid full factorial design is an experimental strategy that applies a full factorial structure to a selected subset of factors — those believed to have the strongest interactions — while treating remaining factors with a reduced or fractional scheme. This hybrid approach balances the complete interaction information of a full factorial with the run-count efficiency of fractional designs, making it practical for studies with many factors where a pure full factorial would be prohibitively expensive. | 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. |
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