পদ্ধতির তুলনা করুন

	বেয়েশীয় তথ্য মানদণ্ড (BIC)	গড় বর্গ ত্রুটি (MSE)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1978	1809
প্রবর্তক	Gideon E. Schwarz	Carl Friedrich Gauss
ধরন	Bayesian model selection metric	Squared-error loss function
মৌলিক উৎস	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.
অপর নাম	BIC, Schwarz criterion, Schwarz information criterion	MSE, L2 error, quadratic error
সম্পর্কিত	4	4
সারসংক্ষেপ	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.