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Bayesian model averaging with missing data

Bayesian Model Averaging with missing data (BMA-MD) simultaneously addresses two sources of uncertainty: which model best describes the data, and what the unobserved values are. Rather than selecting a single imputed dataset and a single model, the approach averages predictions across the full space of candidate models and plausible completions of the missing values, propagating both sources of uncertainty into every estimate and prediction.

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Bayesian Model Averaging with Missing Data

Taksonomiskās metodes reģistrs · bayesian / bayesian

Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417. · URL
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, New York. · ISBN 978-0471655749

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Taxonomic bucketApproximate Bayesian Computation with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Hierarchical Model with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Inference with Missing Datamachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.See alsoMultiple Imputationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlo with Missing Datamachine-suggested · Relational suggestion, not evidence.