Comparar métodos

	Desdobramento da Função Qualidade Bayesiana	Design of Experiments Bayesiano
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	QFD: 1966–1972; Bayesian QFD extensions: 2000s–present	1956 (foundational); formalized 1970s–1990s
Autor original	Yoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleagues	Lindley (1956); Chaloner & Verdinelli (1995) landmark review
Tipo	Probabilistic customer-driven design planning method	Bayesian optimal experimental design
Fonte seminal	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.	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.
Outros nomes	Bayesian QFD, Probabilistic QFD, Bayesian House of Quality, Bayesian Voice of the Customer Analysis	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE
Relacionados	5	3
Resumo	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.