Sammenlign metoder

	Gibbs Sampling med manglende data	Bayesiansk inferens med manglende data
Fagområde	Bayesiansk	Bayesiansk
Familie	Bayesian methods	Bayesian methods
Oprindelsesår	1987–1990	1976–1987
Ophavsperson	Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Type	Bayesian computational method	Bayesian probabilistic model
Oprindelig kilde	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-Interscience. ISBN: 978-0471183860
Aliasser	data augmentation Gibbs sampler, Gibbs sampler with data augmentation, Bayesian imputation via Gibbs sampling, MCMC missing data imputation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Relaterede	6	6
Resumé	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.	Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.