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| 실험 설계의 베이즈 최적화× | 중심합성계획× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1956 (foundational); formalized 1970s–1990s | 1951 |
| 창시자≠ | Lindley (1956); Chaloner & Verdinelli (1995) landmark review | George E. P. Box and K. B. Wilson |
| 유형≠ | Bayesian optimal experimental design | Response surface experimental design |
| 원전≠ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ | 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. DOI ↗ |
| 별칭 | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| 관련 | 3 | 3 |
| 요약≠ | 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. |
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