방법 비교

	결측치가 있는 베이지안 모델 평균화	Bayesian Model Averaging
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1999 (BMA seminal); 2000s (missing-data extensions)	1999
창시자	Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others	Hoeting, Madigan, Raftery & Volinsky
유형	Bayesian ensemble inference under incomplete data	Bayesian model averaging
원전	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.
별칭	BMA with missing data, Bayesian model averaging under missingness, BMA-MI, model-averaged imputation	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
관련	6	5
요약	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.