Comparer des méthodes

	Modélisation bayésienne hiérarchique par moyenne (MBH)	Moyenne Bayésienne de Modèles
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1999–2000s	1999
Auteur d'origine	Extension formalised by Hoeting, Madigan, Raftery, and Volinsky; hierarchical application developed through 1990s–2000s Bayesian literature	Hoeting, Madigan, Raftery & Volinsky
Type	Bayesian model averaging within hierarchical models	Bayesian model averaging
Source fondatrice	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–417.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
Alias	HBMA, hierarchical BMA, multilevel Bayesian model averaging, Bayesian model averaging in hierarchical models	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Apparentées	5	5
Résumé	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.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.