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	MCMC z brakującymi danymi	Algorytm Metropolisa-Hastingsa
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1987	1953
Twórca	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Metropolis et al. (1953); generalised by Hastings (1970)
Typ	Bayesian computational method	Markov chain Monte Carlo sampler
Źródło pierwotne	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.
Inne nazwy	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler
Pokrewne	6	5
Podsumowanie	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.