Сравнение методов

	Бутстреп-симуляция при наличии пропущенных данных	Байесовский вывод при наличии пропущенных данных
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1979–1990s	1976–1987
Автор метода	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Тип	Resampling simulation	Bayesian probabilistic model
Основополагающий источник	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Другие названия	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Связанные	5	6
Сводка	Bootstrap simulation with missing data combines resampling-based variance estimation with principled handling of incomplete observations. Rather than deleting cases or assuming complete data, the method integrates imputation or weighting directly into the bootstrap loop, propagating the additional uncertainty due to missingness into the final standard errors and confidence intervals.	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.