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Bayesiansk informationskriterium (BIC)×Determinationskoefficienten (R²)×
FagområdeModelevalueringModelevaluering
FamilieMCDMMCDM
Oprindelsesår19781896
OphavspersonGideon E. SchwarzKarl Pearson
TypeBayesian model selection metricGoodness-of-fit metric
Oprindelig kildeSchwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. DOI ↗Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318. link ↗
AliasserBIC, Schwarz criterion, Schwarz information criterionR², coefficient of determination, r2 score
Relaterede45
ResuméThe 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.The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.
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ScholarGateSammenlign metoder: Bayesian Information Criterion · R-squared. Hentet 2026-06-17 fra https://scholargate.app/da/compare