Open access
Aug 2026
Unbiased kinetic Langevin Monte Carlo with inexact gradients
Theoretical analysis demonstrates that the proposed estimator is unbiased, attains finite variance, and satisfies a central limit theorem, and the results demonstrate that in large-scale applications, the unbiased algorithm can be 2–3 orders of magnitude more efficient than the “gold-standard” randomized Hamiltonian Monte Carlo.
Neil K. Chada, B. Leimkuhler, Daniel Paulin et al.
· Annals of Statistics · 0 citations