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- Title
Convergence Rates of Attractive-Repulsive MCMC Algorithms.
- Authors
Jiang, Yu Hang; Liu, Tong; Lou, Zhiya; Rosenthal, Jeffrey S.; Shangguan, Shanshan; Wang, Fei; Wu, Zixuan
- 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 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.
- Subjects
MARKOV chain Monte Carlo; ALGORITHMS; PARTICLE swarm optimization
- Publication
Methodology & Computing in Applied Probability, 2022, Vol 24, Issue 3, p2029
- ISSN
1387-5841
- Publication type
Article
- DOI
10.1007/s11009-021-09909-y