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| Quality Function Deployment Bayes (Bayesian QFD)× | Thiết kế thí nghiệm Bayes× | |
|---|---|---|
| Lĩnh vực | Thiết kế thí nghiệm | Thiết kế thí nghiệm |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | QFD: 1966–1972; Bayesian QFD extensions: 2000s–present | 1956 (foundational); formalized 1970s–1990s |
| Người khởi xướng≠ | Yoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleagues | Lindley (1956); Chaloner & Verdinelli (1995) landmark review |
| Loại≠ | Probabilistic customer-driven design planning method | Bayesian optimal experimental design |
| Công trình gốc≠ | Tang, J., Fung, R. Y. K., Xu, B., & Wang, D. (2002). A new approach to quality function deployment planning with financial consideration. Computers & Operations Research, 29(11), 1447–1463. DOI ↗ | Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. DOI ↗ |
| Tên gọi khác | Bayesian QFD, Probabilistic QFD, Bayesian House of Quality, Bayesian Voice of the Customer Analysis | Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE |
| Liên quan≠ | 5 | 3 |
| Tóm tắt≠ | Bayesian Quality Function Deployment (Bayesian QFD) integrates Bayesian probabilistic inference into the classical House of Quality framework to handle uncertainty in customer preference data and relationship matrices. By expressing relationship weights and importance ratings as probability distributions rather than point estimates, it propagates uncertainty through the planning process and yields more defensible engineering prioritization decisions under incomplete or conflicting customer information. | 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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