方法对比
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| 贝叶斯全因子设计× | 中心复合设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher) | 1951 |
| 提出者≠ | Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles | George E. P. Box and K. B. Wilson |
| 类型≠ | Bayesian experimental design method | Response surface experimental design |
| 开创性文献≠ | 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 ↗ |
| 别名 | Bayesian FFD, Bayesian complete factorial experiment, Bayesian full factorial experiment, Bayesian all-combinations design | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| 相关 | 3 | 3 |
| 摘要≠ | Bayesian full factorial design combines the complete combinatorial structure of classical full factorial experiments — running every combination of factor levels — with a Bayesian inferential framework that incorporates prior knowledge about factor effects and yields full posterior distributions over main effects, interactions, and model parameters, rather than point estimates and p-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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