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| Bayesiansk Six Sigma DMAIC× | Bayesiansk design af eksperimenter× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1986 (DMAIC); Bayesian integration circa 1995–2010 | 1956 (foundational); formalized 1970s–1990s |
| Ophavsperson≠ | Six Sigma: Bill Smith / Mikel Harry at Motorola (1986); Bayesian integration developed in quality literature through 1990s–2000s | Lindley (1956); Chaloner & Verdinelli (1995) landmark review |
| Type≠ | Hybrid quality-improvement framework | Bayesian optimal experimental design |
| Oprindelig kilde≠ | Pan, J.-N. (2007). Bayesian approach to estimation of process capability indices in process quality assurance. Quality and Reliability Engineering International, 23(1), 3–14. link ↗ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ |
| Aliasser | Bayesian DMAIC, Bayesian Six Sigma, B-DMAIC, Probabilistic Six Sigma DMAIC | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE |
| Relaterede≠ | 6 | 3 |
| Resumé≠ | Bayesian Six Sigma DMAIC integrates Bayesian statistical inference into the classical Define-Measure-Analyze-Improve-Control quality-improvement framework. Rather than relying solely on frequentist hypothesis tests and point estimates, it incorporates prior knowledge — from expert judgment, historical production data, or pilot studies — and updates beliefs about process parameters as new data arrive. The result is a more adaptive, uncertainty-aware approach to reducing defects and improving process capability, particularly valuable when sample sizes are small or prior domain knowledge is rich. | 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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