Сравнение методов

	Метрополис-Гастингс с пропущенными данными	Байесовский вывод при наличии пропущенных данных
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1953 / 1987	1976–1987
Автор метода	Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Тип	MCMC sampler with latent-variable augmentation	Bayesian probabilistic model
Основополагающий источник	Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540.	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Другие названия	MH with missing data, Metropolis-Hastings data augmentation, MCMC missing data imputation, MH data-augmentation sampler	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Связанные	6	6
Сводка	Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step.	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.