Regeneration in Markov Chain Samplers
本文讨论如何利用马尔可夫链分裂技术在采样器中引入再生，从而用再生方法分析输出并诊断采样器性能，适用于Metropolis等采样器。
Abstract Markov chain sampling has recently received considerable attention, in particular in the context of Bayesian computation and maximum likelihood estimation. This article discusses the use of Markov chain splitting, originally developed for the theoretical analysis of general state-space Markov chains, to introduce regeneration into Markov chain samplers. This allows the use of regenerative methods for analyzing the output of these samplers and can provide a useful diagnostic of sampler performance. The approach is applied to several samplers, including certain Metropolis samplers that can be used on their own or in hybrid samplers, and is illustrated in several examples.