Comparer des méthodes

	Moyennage bayésien de modèles de séries chronologiques	Moyenne Bayésienne de Modèles
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1999–2010	1999
Auteur d'origine	Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions	Hoeting, Madigan, Raftery & Volinsky
Type	Bayesian ensemble / model combination	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), 382–401.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
Alias	TS-BMA, Bayesian model averaging for time series, BMA forecasting, time series BMA	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Apparentées	5	5
Résumé	Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BMA integrates over model uncertainty, producing forecasts that are more robust and better calibrated than any single model alone.	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.