Vertaile menetelmiä

	Bayesiläinen mallien keskiarvoistaminen puuttuvalla datalla	Monitahinen imputointi
Tieteenala	Bayesilainen tilastotiede	Tilastotiede
Menetelmäperhe	Bayesian methods	Process / pipeline
Syntyvuosi	1999 (BMA seminal); 2000s (missing-data extensions)	1987
Kehittäjä	Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others	Donald B. Rubin
Tyyppi	Bayesian ensemble inference under incomplete data	Missing-data handling procedure
Alkuperäislähde	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417.	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
Rinnakkaisnimet	BMA with missing data, Bayesian model averaging under missingness, BMA-MI, model-averaged imputation	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
Liittyvät	6	1
Tiivistelmä	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.	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.