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Metropolis-Hastings with Missing Data

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.

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Metropolis-Hastings Algorithm with Missing Data Augmentation

Taksonoomiline meetodikirje · bayesian / bayesian

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 10.2307/2289457
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. · ISBN 978-1439840955

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Taxonomic bucketBayesian Inference with Missing Datamachine-suggested · Relational suggestion, not evidence.See alsoData Augmentationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Sampling with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHamiltonian Monte Carlo with Missing Datamachine-suggested · Relational suggestion, not evidence.Same method familyMetropolis-Hastings Algorithmmachine-suggested · Relational suggestion, not evidence.See alsoMultiple Imputationmachine-suggested · Relational suggestion, not evidence.