Сравнение методов

	Байесовский вывод при наличии пропущенных данных	MCMC с пропущенными данными
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1976–1987	1987
Автор метода	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
Тип	Bayesian probabilistic model	Bayesian computational method
Основополагающий источник	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
Другие названия	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
Связанные	6	6
Сводка	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.	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.