Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
We represent the abstract Hamiltonian (Hybrid) Monte Carlo (HMC) algorithm as iterations of an operator on densities in a Hilbert space, and recognize two invariant properties of Hamiltonian motion sufficient for convergence. Under a mild coverage assumption, we present a proof of strong convergence of the algorithm to the target density. The proof relies on the self-adjointness of the operator, and we extend the result to the general case of the motions beyond Hamiltonian ones acting on a finite dimensional space, to the motions acting an abstract space equipped with a reference measure, as long as they satisfy the two sufficient properties. For standard Hamiltonian motion, the convergence is also geometric in the case when the target density satisfies a log-convexity condition.
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
Hongzhi Wang, Tanveer Syeda-Mahmood
Big Data 2024
Seung Gu Kang, Jeff Weber, et al.
ACS Fall 2023
Hiroki Yanagisawa, Kohei Miyaguchi, et al.
NeurIPS 2022