Salīdzināt metodes

	Hierarhiskā Bayes'a modeļu vidēšana	Bayesian Information Criterion (BIC)
Nozare	Bajesa metodes	Modeļu novērtēšana
Saime	Bayesian methods	MCDM
Izcelsmes gads	1999–2000s	1978
Autors	Extension formalised by Hoeting, Madigan, Raftery, and Volinsky; hierarchical application developed through 1990s–2000s Bayesian literature	Gideon E. Schwarz
Tips	Bayesian model averaging within hierarchical models	Bayesian model selection metric
Pirmavots	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–417.	Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.
Citi nosaukumi	HBMA, hierarchical BMA, multilevel Bayesian model averaging, Bayesian model averaging in hierarchical models	BIC, Schwarz criterion, Schwarz information criterion
Saistītās	5	4
Kopsavilkums	Hierarchical Bayesian model averaging (HBMA) combines Bayesian model averaging with hierarchical model structure, averaging posterior quantities over a set of candidate models weighted by each model's posterior probability. Rather than selecting a single best model, HBMA propagates model uncertainty through a hierarchical framework, producing predictions and parameter estimates that honestly reflect uncertainty about which model is correct.	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.