On random- and systematic-scan samplers
本文引入一种简单的时间齐次马尔可夫嵌入，用于分析系统扫描采样器，证明在渐近方差准则下，涉及两个因子的系统扫描采样器总是优于随机扫描采样器。
We introduce a simple time-homogeneous Markov embedding of a class of time-inhomogeneous Markov chains widely used in the context of Monte Carlo sampling algorithms, such as systematic-scan Metropolis-within-Gibbs samplers. This allows us to establish that systematic-scan samplers involving two factors are always better than their random-scan counterparts, when asymptotic variance is the criterion of interest. We also show that this embedding sheds some light on the result of Maire et al. (2014) and discuss the scenario involving more than two factors.