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| Thiết kế Thừa số Toàn bộ Bayes× | Thiết kế thí nghiệm Bayes× | |
|---|---|---|
| Lĩnh vực | Thiết kế thí nghiệm | Thiết kế thí nghiệm |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher) | 1956 (foundational); formalized 1970s–1990s |
| Người khởi xướng≠ | Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles | Lindley (1956); Chaloner & Verdinelli (1995) landmark review |
| Loại≠ | Bayesian experimental design method | Bayesian optimal experimental design |
| Công trình gốc | Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304. DOI ↗ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ |
| Tên gọi khác | Bayesian FFD, Bayesian complete factorial experiment, Bayesian full factorial experiment, Bayesian all-combinations design | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE |
| Liên quan | 3 | 3 |
| Tóm tắt≠ | 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. | 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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