Bayesian methodsBayesian / computational

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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Sources

  1. 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
  2. Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434. link

Related methods

ScholarGateRobust Hamiltonian Monte Carlo (Robust Hamiltonian Monte Carlo). Retrieved 2026-06-04 from https://scholargate.app/en/bayesian/robust-hamiltonian-monte-carlo