Neil K. Chada

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.