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Hierarchical Bayesian Model Averaging/Evidence
Method evidence record

Hierarchical Bayesian Model Averaging

Hierarchical Bayesian model averaging (HBMA) combines Bayesian model averaging with hierarchical model structure, averaging posterior quantities over a set of candidate models weighted by each model's posterior probability. Rather than selecting a single best model, HBMA propagates model uncertainty through a hierarchical framework, producing predictions and parameter estimates that honestly reflect uncertainty about which model is correct.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Hierarchical Bayesian Model Averaging
Taxonomic method record · 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
  • Fragoso, T. M., Bertoli, W., & Louzada, F. (2018). Bayesian model averaging: A systematic review and conceptual classification. International Statistical Review, 86(1), 1–28. · DOI 10.1111/insr.12243
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Related methods

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See alsoBayesian Information Criterionmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Markov Chain Monte Carlomachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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