Krahasoni metodat

	Dizajni Box-Behnken Bajezian – RSM Bajezian me Pika Strukturore Tremëshe	Dizenjimi Box-Behnken
Fusha	Dizajni eksperimental	Dizajni eksperimental
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	1960 (BBD); Bayesian integration ~1990s–2000s	1960
Krijuesi	Box & Behnken (classical BBD, 1960); Bayesian extension developed by multiple authors in response surface literature	George E. P. Box and Donald W. Behnken
Lloji	Bayesian response surface experimental design	Response surface design (incomplete three-level factorial)
Burimi themelues	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., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.
Emërtime të tjera	Bayesian BBD, Bayesian RSM Box-Behnken, Bayesian three-level design, BBD with Bayesian optimization	BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial design
Të lidhura	5	3
Përmbledhja	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.	The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test.