手法を比較

	ベイズ情報量基準 (BIC)	平均二乗誤差（MSE）
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	1978	1809
提唱者	Gideon E. Schwarz	Carl Friedrich Gauss
種類	Bayesian model selection metric	Squared-error loss function
原典	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.
別名	BIC, Schwarz criterion, Schwarz information criterion	MSE, L2 error, quadratic error
関連	4	4
概要	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.