Faster Hamiltonian Monte Carlo by learning leapfrog scale: an offline randomized solution
Changye Wu; Pierre Pudlo; Christian P. Robert; Julien Stoehr · 2026 · Statistics and Computing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
stands for empirical, leverages importance sampling to construct an empirical distribution on discretization parameters, thereby eliminating the need for manual burn-in diagnostics and online adaptation. The proposal distribution used in the calibration stage is obtained via a Population Monte Carlo scheme with tempering and relies on flexible parametric variational families such as normalizing flows. Once the calibration stage complete, the resulting algorithm defines a homogeneous Markov chain via a mixture of HMC kernels with a fixed mixing distribution, and hence preserves the target distr
Abstract by Changye Wu; Pierre Pudlo; Christian P. Robert; Julien Stoehr, Statistics and Computing (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s11222-026-10972-z