Comparer des méthodes

	Moyennage Robuste de Modèles Bayésiens	Régression bayésienne
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1999–2012	—
Auteur d'origine	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	—
Type	Bayesian model selection and averaging	Bayesian linear model
Source fondatrice	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	6	2
Résumé	Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.