Comparar métodos

	Design of Experiments Bayesiano	Desenho de Experimentos
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1956 (foundational); formalized 1970s–1990s	1935
Autor original	Lindley (1956); Chaloner & Verdinelli (1995) landmark review	Ronald A. Fisher
Tipo	Bayesian optimal experimental design	Experimental planning framework
Fonte seminal	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Outros nomes	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	3	3
Resumo	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.