Method evidence record
Hierarchical Hamiltonian Monte Carlo
Hierarchical Hamiltonian Monte Carlo (Hierarchical HMC) applies Hamiltonian Monte Carlo sampling to Bayesian hierarchical models, addressing the severe geometric challenges those models pose. By combining non-centered parameterizations with HMC's gradient-driven proposals, it achieves efficient posterior exploration of the multi-level funnel-shaped geometries that standard MCMC methods struggle with.
Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.
Source record
Recorded
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Hamiltonian Monte Carlo for Hierarchical Models
Taxonomic method recordbayesianbayesian
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
Open full method2 recorded citations, copied from the method source record.
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Related methods
Suggested5
Generated from the method graph and shown as machine-suggested relations. No evidence claim is inferred.
- Same method familyBayesian Regression
machine-suggestedRelational suggestion, not evidence.
- Same method familyHamiltonian Monte Carlo
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketHierarchical Bayesian Inference
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketHierarchical Markov Chain Monte Carlo
machine-suggestedRelational suggestion, not evidence.
- Same method familyMCMC
machine-suggestedRelational suggestion, not evidence.