方法对比

	缺失数据下的MCMC	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	1987	1976–1987
提出者	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
类型	Bayesian computational method	Bayesian probabilistic model
开创性文献	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
别名	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
相关	6	6
摘要	MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness.	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.