Сравнение методов

	MCMC с пропущенными данными	Алгоритм Метрополиса
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1987	1953
Автор метода	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Metropolis et al. (1953); generalised by Hastings (1970)
Тип	Bayesian computational method	Markov chain Monte Carlo sampler
Основополагающий источник	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.
Другие названия	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler
Связанные	6	5
Сводка	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.	The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases.