Methoden vergelijken

	Bayesian Box-Behnken Design	Response Surface Methodology (RSM)
Vakgebied	Experimenteel ontwerp	Experimenteel ontwerp
Familie	Process / pipeline	Hypothesis test
Jaar van ontstaan	1960 (BBD); Bayesian integration ~1990s–2000s	1951
Grondlegger	Box & Behnken (classical BBD, 1960); Bayesian extension developed by multiple authors in response surface literature	George E. P. Box & K. B. Wilson
Type	Bayesian response surface experimental design	Second-order polynomial response surface model
Oorspronkelijke bron	Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.	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 BBD, Bayesian RSM Box-Behnken, Bayesian three-level design, BBD with Bayesian optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Verwant	5	7
Samenvatting	Bayesian Box-Behnken Design combines the classical Box-Behnken three-level design structure with Bayesian statistical inference to fit and optimize response surface models. It uses mid-edge and center points to efficiently estimate a second-order polynomial response surface while incorporating prior knowledge about model parameters and propagating uncertainty through to predictions and optimal factor settings. The approach is widely applied in engineering process optimization and formulation studies.	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.