Convergence Rates of Attractive-Repulsive MCMC Algorithms
Probability
2021-09-03 v3
Abstract
We consider MCMC algorithms for certain particle systems which include both attractive and repulsive forces, making their convergence analysis challenging. We prove that a version of these algorithms on a bounded state space is uniformly ergodic with an explicit quantitative convergence rate. We also prove that a version on an unbounded state-space is still geometrically ergodic, and then use the method of shift-coupling to obtain an explicit quantitative bound on its convergence rate.
Cite
@article{arxiv.2012.04786,
title = {Convergence Rates of Attractive-Repulsive MCMC Algorithms},
author = {Yu Hang Jiang and Tong Liu and Zhiya Lou and Jeffrey S. Rosenthal and Shanshan Shangguan and Fei Wang and Zixuan Wu},
journal= {arXiv preprint arXiv:2012.04786},
year = {2021}
}
Comments
26 pages, 2 figures