Comparar métodos

	Diseño Factorial Completo Bayesiano	Diseño Factorial Fraccional Bayesiano
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher)	1990s
Autor original	Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles	DuMouchel & Jones; Chipman, Hamada & Wu
Tipo	Bayesian experimental design method	Bayesian experimental design method
Fuente seminal	Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304.	DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47.
Alias	Bayesian FFD, Bayesian complete factorial experiment, Bayesian full factorial experiment, Bayesian all-combinations design	Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design
Relacionados	3	3
Resumen	Bayesian full factorial design combines the complete combinatorial structure of classical full factorial experiments — running every combination of factor levels — with a Bayesian inferential framework that incorporates prior knowledge about factor effects and yields full posterior distributions over main effects, interactions, and model parameters, rather than point estimates and p-values.	Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded.