Comparer des méthodes

	Moyenne bayésienne de modèles avec erreur de mesure	Régression bayésienne
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1999–2006	—
Auteur d'origine	Hoeting, Madigan, Raftery, Volinsky (BMA); Carroll, Stefanski and colleagues (ME correction)	—
Type	Bayesian ensemble model with covariate error correction	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), 382-417.	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	BMA-ME, BMA with errors-in-variables, Bayesian model averaging errors-in-covariates, measurement error BMA	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	3	2
Résumé	Bayesian model averaging with measurement error (BMA-ME) combines two probabilistic ideas: it averages predictions across competing regression models weighted by each model's posterior probability, while simultaneously accounting for the fact that one or more predictors are observed with random error rather than exactly. The result is a posterior that propagates both model uncertainty and covariate measurement noise into every inference and prediction.	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.