방법 비교

	결측치가 있는 MCMC	결측 데이터가 있는 베이즈 추론
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1987	1976–1987
창시자	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
유형	Bayesian computational method	Bayesian probabilistic model
원전	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
별칭	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
관련	6	6
요약	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.