Comparer des méthodes

	Critère d'information bayésien (BIC)	Erreur quadratique moyenne (EQM)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1978	1809
Auteur d'origine	Gideon E. Schwarz	Carl Friedrich Gauss
Type	Bayesian model selection metric	Squared-error loss function
Source fondatrice	Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
Alias	BIC, Schwarz criterion, Schwarz information criterion	MSE, L2 error, quadratic error
Apparentées	4	4
Résumé	The Bayesian Information Criterion is an information-theoretic model selection criterion that approximates Bayesian model comparison. Introduced by Gideon Schwarz in 1978, BIC penalizes model complexity more heavily than AIC by using a sample-size-dependent penalty, making it particularly suitable for identifying the true underlying model structure.	Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization.