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	Symulacja metodą Monte Carlo z brakującymi danymi	Próbkowanie Gibbsa z brakującymi danymi
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1987–2002	1987–1990
Twórca	Rubin, D. B. / Little, R. J. A.	Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)
Typ	Simulation-based estimation	Bayesian computational method
Źródło pierwotne	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540.
Inne nazwy	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data	data augmentation Gibbs sampler, Gibbs sampler with data augmentation, Bayesian imputation via Gibbs sampling, MCMC missing data imputation
Pokrewne	6	6
Podsumowanie	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.	Gibbs sampling with missing data treats unobserved values as additional unknowns alongside model parameters and samples all of them jointly within a Markov chain Monte Carlo loop. The method alternates between drawing the missing values from their conditional distribution given the parameters and drawing the parameters from their conditional distribution given the completed data, producing a posterior over both simultaneously.