Comparer des méthodes

	Moyenne Bayésienne de Modèles	Régression Lasso
Domaine	Bayésien	Apprentissage automatique
Famille	Bayesian methods	Machine learning
Année d'origine	1999	1996
Auteur d'origine	Hoeting, Madigan, Raftery & Volinsky	Tibshirani, R.
Type	Bayesian model averaging	Regularized linear regression (L1 penalty)
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.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	5	4
Résumé	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.