Сравнение методов

	Метод Монте-Карло для данных с пропусками	MCMC с пропущенными данными
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1987–2002	1987
Автор метода	Rubin, D. B. / Little, R. J. A.	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
Тип	Simulation-based estimation	Bayesian computational method
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. ISBN: 978-0471183860
Другие названия	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
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.	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.