Comparer des méthodes

	Plan d'Expériences Factoriel Complet Bayésien	Conception bayésienne d'expériences
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)	1956 (foundational); formalized 1970s–1990s
Auteur d'origine	Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles	Lindley (1956); Chaloner & Verdinelli (1995) landmark review
Type	Bayesian experimental design method	Bayesian optimal experimental design
Source fondatrice	Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304.	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.
Alias	Bayesian FFD, Bayesian complete factorial experiment, Bayesian full factorial experiment, Bayesian all-combinations design	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE
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 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.