摘要翻译：
蒙特卡罗抽样方法往往存在相关时间长的问题。因此，这些方法必须运行许多步骤才能生成一个独立的样本。本文提出了一种克服这一困难的方法。该方法利用快速平衡的粗马尔可夫链的信息，该粗马尔可夫链对整个系统的边缘分布进行采样。这是通过全链和辅助粗链之间的交换来实现的。本文给出了随机微分方程桥式采样和滤波/平滑问题的数值试验结果。
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英文标题：
《Parallel marginalization Monte Carlo with applications to conditional
  path sampling》
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作者：
Jonathan Weare
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  Monte Carlo sampling methods often suffer from long correlation times. Consequently, these methods must be run for many steps to generate an independent sample. In this paper a method is proposed to overcome this difficulty. The method utilizes information from rapidly equilibrating coarse Markov chains that sample marginal distributions of the full system. This is accomplished through exchanges between the full chain and the auxiliary coarse chains. Results of numerical tests on the bridge sampling and filtering/smoothing problems for a stochastic differential equation are presented.
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PDF链接：
https://arxiv.org/pdf/709.1721