Methoden vergelijken

	Bayesiaans fractioneel factorieel ontwerp	Response Surface Methodology (RSM)
Vakgebied	Experimenteel ontwerp	Experimenteel ontwerp
Familie	Process / pipeline	Hypothesis test
Jaar van ontstaan	1990s	1951
Grondlegger	DuMouchel & Jones; Chipman, Hamada & Wu	George E. P. Box & K. B. Wilson
Type	Bayesian experimental design method	Second-order polynomial response surface model
Oorspronkelijke bron	DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Aliassen	Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design	RSM, Central Composite Design, Box-Behnken Design, CCD
Verwant	3	7
Samenvatting	Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.