Comparar métodos

	Diseño Experimental Bayesiano	Diseño Compuesto Central
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1956 (foundational); formalized 1970s–1990s	1951
Autor original	Lindley (1956); Chaloner & Verdinelli (1995) landmark review	George E. P. Box and K. B. Wilson
Tipo	Bayesian optimal experimental design	Response surface experimental design
Fuente seminal	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.	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.
Alias	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
Relacionados	3	3
Resumen	Bayesian design of experiments selects experimental runs by maximising a utility function — typically the expected information gain — computed over prior beliefs about model parameters. Unlike classical design, which optimizes algebraic criteria such as D-optimality under fixed assumptions, Bayesian DOE incorporates prior knowledge and uncertainty about the system, yielding designs that are optimal in expectation across all plausible parameter values.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.