方法对比

	缺失数据贝叶斯模型平均法	缺失数据的贝叶斯推断
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1999 (BMA seminal); 2000s (missing-data extensions)	1976–1987
提出者	Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
类型	Bayesian ensemble inference under incomplete data	Bayesian probabilistic model
开创性文献	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
别名	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
相关	6	6
摘要	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.