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Multilevel Bayesian Model Averaging

Multilevel Bayesian model averaging (ML-BMA) extends classical Bayesian model averaging to grouped or hierarchically structured data. Rather than committing to a single multilevel model specification, it computes a weighted average of predictions and parameter estimates across a set of candidate multilevel models, weighting each model by its posterior probability given the data. The result accounts simultaneously for uncertainty in the grouping structure, fixed effects, random effects, and covariate selection.

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Taksonomski zapis metode · bayesian / bayesian

Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. · URL
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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Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultilevel MCMCmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultilevel Variational Inferencemachine-suggested · Relational suggestion, not evidence.