Zapis dokaza metode

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.

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Izvorni zapis

Citati kopirani doslovno iz izvornog zapisa metode. Ne impliciraju nikakvu provjeru na razini tvrdnje.

Markov Chain Monte Carlo with Missing Data

Taksonomski zapis metode · 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

Otvori cijelu metodu

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Nema uređenih tvrdnji

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Povezane metode

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Same method familyBayesian Hierarchical Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Inference with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Same method familyMetropolis-Hastings Algorithmmachine-suggested · Relational suggestion, not evidence.See alsoMultiple Imputationmachine-suggested · Relational suggestion, not evidence.