Comparer des méthodes

	Plan d'Expériences Factoriel Complet Bayésien	Plan factoriel fractionnaire bayésien
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher)	1990s
Auteur d'origine	Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles	DuMouchel & Jones; Chipman, Hamada & Wu
Type	Bayesian experimental design method	Bayesian experimental design method
Source fondatrice	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
Apparentées	3	3
Résumé	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.