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New HMC methods improve sampling for complex distributions

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A new paper introduces locally adaptive step sizes for Hamiltonian Monte Carlo (HMC) methods, aiming to improve sampling performance for complex target distributions. The research, building on prior work by Bou-Rabee et al., details the adaptive step size procedure under broader assumptions and proposes alternative error measures to reduce reversibility failure rejections. The paper also extends these adaptive step sizes to standard HMC and Metropolis-corrected kinetic Langevin/generalized HMC samplers, supported by a numerical study comparing different methods. AI

RANK_REASON The cluster contains a research paper detailing new computational methods. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New HMC methods improve sampling for complex distributions

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The cluster contains a research paper detailing new computational methods. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jimmy Huy Tran, Tore Selland Kleppe, Nawaf Bou-Rabee ·

    Locally adaptive step sizes for Hamiltonian Monte Carlo methods

    arXiv:2610.16506v1 Announce Type: cross Abstract: Exact reversible locally adaptive step sizes are introduced by Bou-Rabee et al. (2026) in the context of the No-U-Turn Hamiltonian Monte Carlo (HMC) sampler for the purpose of high performance HMC sampling in challenging target di…

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