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	Uśrednianie modeli bayesowskich z brakującymi danymi	Bayesowska inferencja przy brakujących danych
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1999 (BMA seminal); 2000s (missing-data extensions)	1976–1987
Twórca	Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Typ	Bayesian ensemble inference under incomplete data	Bayesian probabilistic model
Źródło pierwotne	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417.	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Inne nazwy	BMA with missing data, Bayesian model averaging under missingness, BMA-MI, model-averaged imputation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Pokrewne	6	6
Podsumowanie	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 inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.