方法对比

	贝叶斯信息准则 (BIC)	均方误差 (MSE)
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1978	1809
提出者	Gideon E. Schwarz	Carl Friedrich Gauss
类型	Bayesian model selection metric	Squared-error loss function
开创性文献	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.
别名	BIC, Schwarz criterion, Schwarz information criterion	MSE, L2 error, quadratic error
相关	4	4
摘要	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.