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| Hierarkisk Bayesiansk modellmedling× | Bayesianskt informationskriterium (BIC)× | |
|---|---|---|
| Ämnesområde≠ | Bayesiansk statistik | Modellutvärdering |
| Familj≠ | Bayesian methods | MCDM |
| Ursprungsår≠ | 1999–2000s | 1978 |
| Upphovsperson≠ | Extension formalised by Hoeting, Madigan, Raftery, and Volinsky; hierarchical application developed through 1990s–2000s Bayesian literature | Gideon E. Schwarz |
| Typ≠ | Bayesian model averaging within hierarchical models | Bayesian model selection metric |
| Ursprungskälla≠ | Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–417. link ↗ | Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. DOI ↗ |
| Alias≠ | HBMA, hierarchical BMA, multilevel Bayesian model averaging, Bayesian model averaging in hierarchical models | BIC, Schwarz criterion, Schwarz information criterion |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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. |
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