Convergence Rates of Attractive-Repulsive MCMC Algorithms

被引：0

作者：

Jiang, Yu Hang ^{[1
]}

Liu, Tong ^{[1
]}

Lou, Zhiya ^{[1
]}

Rosenthal, Jeffrey S. ^{[1
]}

Shangguan, Shanshan ^{[1
]}

Wang, Fe ^{[1
]}

Wu, Zixuan ^{[1
]}

机构：

[1] Univ Toronto, Dept Stat Sci, 700 Univ Ave,9th Floor, Toronto, ON M5G 1X6, Canada

来源：

METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY | 2022年 / 24卷 / 03期

关键词：

Markov chain Monte Carlo; Convergence rate; Particle system; Shift coupling; MONTE-CARLO METHODS; MARKOV-CHAIN; STOCHASTICALLY MONOTONE; DISTRIBUTIONS; EXPLORATION;

D O I：

10.1007/s11009-021-09909-y

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 explicit quantitative onvergence 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.

引用

页码：2029 / 2054

页数：26

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