方法对比
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| 贝叶斯分数因子设计× | 贝叶斯实验设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s | 1956 (foundational); formalized 1970s–1990s |
| 提出者≠ | DuMouchel & Jones; Chipman, Hamada & Wu | Lindley (1956); Chaloner & Verdinelli (1995) landmark review |
| 类型≠ | Bayesian experimental design method | Bayesian optimal experimental design |
| 开创性文献≠ | DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47. DOI ↗ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ |
| 别名 | Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE |
| 相关 | 3 | 3 |
| 摘要≠ | Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded. | 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. |
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