Сравнение методов

	Метод Монте-Карло для данных с пропусками	Байесовский вывод при наличии пропущенных данных
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1987–2002	1976–1987
Автор метода	Rubin, D. B. / Little, R. J. A.	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Тип	Simulation-based estimation	Bayesian probabilistic model
Основополагающий источник	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
Другие названия	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Связанные	6	6
Сводка	Monte Carlo simulation with missing data combines stochastic simulation — drawing random values from probability distributions — with principled missing-data strategies such as multiple imputation. Instead of discarding incomplete records or substituting a single fill-in value, the method generates many simulated complete datasets, runs the target analysis on each, and pools the results to yield estimates that honestly reflect both sampling uncertainty and uncertainty due to 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.