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Akaikeov kriterijum informacije (AIC)×Bejzijanski informacioni kriterijum (BIC)×
OblastEvaluacija modelaEvaluacija modela
PorodicaMCDMMCDM
Godina nastanka19741978
TvoracHirotugu AkaikeGideon E. Schwarz
TipModel selection metricBayesian model selection metric
Temeljni izvorAkaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. DOI ↗Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. DOI ↗
Drugi naziviAICBIC, Schwarz criterion, Schwarz information criterion
Srodne44
SažetakThe Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.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.
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ScholarGateUporedite metode: Akaike Information Criterion · Bayesian Information Criterion. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare