论文标题
了解马尔可夫链中的Linchpin变量蒙特卡洛
Understanding Linchpin Variables in Markov Chain Monte Carlo
论文作者
论文摘要
Markov中使用Linchpin变量的介绍 提供了连锁蒙特卡洛（MCMC）。在普遍之前 采用MCMC方法，使用Linchpin的条件采样 变量本质上是模拟的唯一实用方法 来自多元分布。随着MCMC的出现，Linchpin 变量在很大程度上被忽略了。但是，有一个 与MCMC结合使用它们的兴趣复兴 方法，有充分的理由这样做。简单 提供了该方法的推导，其有效性，收益和 讨论了局限性，研究中的一些例子 介绍了文学。
An introduction to the use of linchpin variables in Markov chain Monte Carlo (MCMC) is provided. Before the widespread adoption of MCMC methods, conditional sampling using linchpin variables was essentially the only practical approach for simulating from multivariate distributions. With the advent of MCMC, linchpin variables were largely ignored. However, there has been a resurgence of interest in using them in conjunction with MCMC methods and there are good reasons for doing so. A simple derivation of the method is provided, its validity, benefits, and limitations are discussed, and some examples in the research literature are presented.
