Gibbs Sampling with Missing Data
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.
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Източници
- 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. DOI: 10.1080/01621459.1987.10478458 ↗
- Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
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ScholarGate. (2026, June 3). Gibbs Sampling with Missing Data Imputation. ScholarGate. https://scholargate.app/bg/bayesian/gibbs-sampling-with-missing-data
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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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