Method evidence record
MCMC with missing data
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.
Source record
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Markov Chain Monte Carlo with Missing Data
Taxonomic method record · bayesian / bayesian
- Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. · ISBN 978-0471183860
- 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
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