Salīdzināt metodes

	MCMC ar trūkstošiem datiem	Beijiešu secinājumi ar trūkstošiem datiem
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1987	1976–1987
Autors	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Tips	Bayesian computational method	Bayesian probabilistic model
Pirmavots	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Citi nosaukumi	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Saistītās	6	6
Kopsavilkums	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.	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.