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Robust Hamiltonian Monte Carlo/Evidence
Method evidence record

Robust Hamiltonian Monte Carlo

Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler.

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Source record

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

Robust Hamiltonian Monte Carlo
Taxonomic method record · bayesian / bayesian
  • Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523. · DOI 10.1111/rssb.12482
  • Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Same method familyVariational Inferencemachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

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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