Weare Jonathan
Department of Mathematics, University of California, Berkeley, CA 94720, USA.
Proc Natl Acad Sci U S A. 2007 Jul 31;104(31):12657-62. doi: 10.1073/pnas.0705418104. Epub 2007 Jul 19.
Markov chain 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.
马尔可夫链蒙特卡罗抽样方法常常存在较长的关联时间。因此，这些方法必须运行许多步才能生成一个独立样本。本文提出了一种方法来克服这一困难。该方法利用来自快速平衡的粗马尔可夫链的信息，这些链对整个系统的边际分布进行抽样。这是通过全链与辅助粗链之间的交换来实现的。文中给出了关于一个随机微分方程的桥抽样以及滤波/平滑问题的数值测试结果。
