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| 베이즈 통계적 공정 관리× | 실험 설계의 베이즈 최적화× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1950s (foundations); formalized 1990s–2000s | 1956 (foundational); formalized 1970s–1990s |
| 창시자≠ | Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework) | Lindley (1956); Chaloner & Verdinelli (1995) landmark review |
| 유형≠ | Bayesian process monitoring technique | Bayesian optimal experimental design |
| 원전≠ | Menzefricke, U. (2002). On the evaluation of control chart factors for monitoring the process mean and variance. Journal of Quality Technology, 34(2), 167–178. link ↗ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ |
| 별칭 | Bayesian SPC, Bayesian process monitoring, B-SPC, Bayesian control charting | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE |
| 관련≠ | 5 | 3 |
| 요약≠ | Bayesian Statistical Process Control (Bayesian SPC) extends classical SPC by replacing fixed, frequentist control limits with a probabilistic framework that incorporates prior knowledge about the process. Rather than waiting for a run of points to exceed a pre-set 3-sigma boundary, Bayesian SPC continuously updates the probability that the process has shifted given the incoming data, enabling earlier and more informed detection of out-of-control states while formally accounting for uncertainty in process parameters. | 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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