English

Coupling and Convergence for Hamiltonian Monte Carlo

Probability 2020-07-30 v2 Computation Machine Learning

Abstract

Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate is explicit. Global convexity of the potential is not required, and thus multimodal target distributions are included. Explicit quantitative bounds for the number of steps required to approximate the stationary distribution up to a given error are a direct consequence of contractivity. These bounds show that HMC can overcome diffusive behaviour if the duration of the Hamiltonian dynamics is adjusted appropriately.

Keywords

Cite

@article{arxiv.1805.00452,
  title  = {Coupling and Convergence for Hamiltonian Monte Carlo},
  author = {Nawaf Bou-Rabee and Andreas Eberle and Raphael Zimmer},
  journal= {arXiv preprint arXiv:1805.00452},
  year   = {2020}
}

Comments

50 pages, 8 figures, extended the coupling approach to include corresponding results under a Foster-Lyapunov condition

R2 v1 2026-06-23T01:41:55.123Z