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Multilevel Variational Inference/Evidence
Method evidence record

Multilevel Variational Inference

Multilevel variational inference (MLVI) is a scalable approximate Bayesian method that fits hierarchical (multilevel) models by optimizing a variational approximation to the posterior, rather than drawing MCMC samples. It exploits the grouped structure of multilevel data — individuals nested within groups, groups nested within higher-level units — to derive efficient coordinate-wise updates, making Bayesian inference tractable for large clustered datasets.

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Multilevel Variational Inference for Hierarchical Bayesian Models
Taxonomic method record · bayesian / bayesian
  • Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. · DOI 10.1080/01621459.2017.1285773
  • Ranganath, R., Altosaar, J., Tran, D., & Blei, D. M. (2016). Operator variational objectives. Advances in Neural Information Processing Systems, 29. Curran Associates. · URL
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Related methods

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Same method familyBayesian Hierarchical Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultilevel MCMCmachine-suggested · Relational suggestion, not evidence.Same method familyVariational Inferencemachine-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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