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Bayesian information criterion

In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among a finite set of models; models with lower BIC are generally preferred. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).

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Gaussian special case

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Bayesian information criterion

Nodes36
Edges35
Triples33
Avg. degree1.94
Density0.055556
Components1

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Bayesian information criterion

Top relations

related to Further reading · 33
Bayesian information criterion → American Statistical Association, Annals, Archived, Bayesian, BF00053369, Bhat, Bibcode, BIC, Counterexamples, Findley, Information, Institute, Journal, JSTOR, Kass, Kumar, L74, L78, Liddle, March

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Important terminology

bic displaystyle model parameters theta models mid information likelihood criterion function pi bayesian number widehat selection schwarz variance prior lower

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Bayesian information criterionrelated to Further readingBhat0.60section
Bayesian information criterionrelated to Further readingKumar0.60section
Bayesian information criterionrelated to Further readingOn0.60section
Bayesian information criterionrelated to Further readingPDF0.60section
Bayesian information criterionrelated to Further readingArchived0.60section
Bayesian information criterionrelated to Further readingMarch0.60section
Bayesian information criterionrelated to Further readingFindley0.60section
Bayesian information criterionrelated to Further readingCounterexamples0.60section
Bayesian information criterionrelated to Further readingBIC0.60section
Bayesian information criterionrelated to Further readingAnnals0.60section
Bayesian information criterionrelated to Further readingInstitute0.60section
Bayesian information criterionrelated to Further readingStatistical Mathematics0.60section

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