পদ্ধতির তুলনা করুন

	অজানা ডেটা সহ গিবস স্যাম্পলিং	অনুপস্থিত তথ্য সহ এমসিএমসি
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	1987–1990	1987
প্রবর্তক	Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
ধরন	Bayesian computational method	Bayesian computational method
মৌলিক উৎস	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.	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
অপর নাম	data augmentation Gibbs sampler, Gibbs sampler with data augmentation, Bayesian imputation via Gibbs sampling, MCMC missing data imputation	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	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.