方法对比

	贝叶斯六西格玛 DMAIC	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	1986 (DMAIC); Bayesian integration circa 1995–2010	1956 (foundational); formalized 1970s–1990s
提出者	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
类型	Hybrid quality-improvement framework	Bayesian optimal experimental design
开创性文献	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.	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.
别名	Bayesian DMAIC, Bayesian Six Sigma, B-DMAIC, Probabilistic Six Sigma DMAIC	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE
相关	6	3
摘要	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.