Fast mixing of metropolis-hastings with unimodal targets

Electronic Communications in Probability

James Johndrow¹ 

关键词: Markov chain Monte Carlo;    mixing;    geometric ergodicity;   

DOI  :  10.1214/18-ECP170

学科分类：统计和概率

来源: Institute of Mathematical Statistics

【 摘 要 】

A well-known folklore result in the MCMC community is that the Metropolis-Hastings algorithm mixes quickly for any unimodal target, as long as the tails are not too heavy. Although we’ve heard this fact stated many times in conversation, we are not aware of any quantitative statement of this result in the literature, and we are not aware of any quick derivation from well-known results. The present paper patches this small gap in the literature, providing a generic bound based on the popular “drift-and-minorization” framework of [19]. Our main contribution is to study two sublevel sets of the Lyapunov function and use path arguments in order to obtain a sharper bound than what can typically be obtained from multistep minorization arguments.

【 授权许可】

CC BY