Methoden vergelijken

	Bayesian Model Averaging met ontbrekende data	Bayesian Model Averaging
Vakgebied	Bayesiaanse statistiek	Bayesiaanse statistiek
Familie	Bayesian methods	Bayesian methods
Jaar van ontstaan	1999 (BMA seminal); 2000s (missing-data extensions)	1999
Grondlegger	Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others	Hoeting, Madigan, Raftery & Volinsky
Type	Bayesian ensemble inference under incomplete data	Bayesian model averaging
Oorspronkelijke bron	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.
Aliassen	BMA with missing data, Bayesian model averaging under missingness, BMA-MI, model-averaged imputation	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Verwant	6	5
Samenvatting	Bayesian Model Averaging with missing data (BMA-MD) simultaneously addresses two sources of uncertainty: which model best describes the data, and what the unobserved values are. Rather than selecting a single imputed dataset and a single model, the approach averages predictions across the full space of candidate models and plausible completions of the missing values, propagating both sources of uncertainty into every estimate and prediction.	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.