Σύγκριση μεθόδων

	Metropolis-Hastings με Ελλείποντα Δεδομένα	Αλγόριθμος Metropolis-Hastings
Πεδίο	Μπεϋζιανή Στατιστική	Μπεϋζιανή Στατιστική
Οικογένεια	Bayesian methods	Bayesian methods
Έτος προέλευσης	1953 / 1987	1953
Δημιουργός	Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)	Metropolis et al. (1953); generalised by Hastings (1970)
Τύπος	MCMC sampler with latent-variable augmentation	Markov chain Monte Carlo sampler
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	MH with missing data, Metropolis-Hastings data augmentation, MCMC missing data imputation, MH data-augmentation sampler	MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler
Συναφείς	6	5
Σύνοψη	Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step.	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.