Efficient block sampling strategies for sequential Monte Carlo methods
Efficient block sampling strategies for sequential Monte Carlo methods
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DOI：
10.1198/106186006x142744
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发表时间：
2006-09-01
影响因子：
2.4
通讯作者：
Stephane, Senecal
中科院分区：
文献类型：
--
作者：
Doucet, Arnaud;Briers, Mark;Stephane, Senecal
序贯蒙特卡罗（SMC）方法是一套强大的基于模拟的技术，用于从一系列复杂概率分布中顺序采样。这些方法依赖于重要性抽样和重抽样技术的结合。在马尔可夫链蒙特卡罗（MCMC）框架中，如果可以设计“好的”建议分布来更新变量块，块采样策略通常比基于一次采样策略的算法表现得更好。在SMC框架中，标准算法一次对一个变量进行顺序采样，而与MCMC一样，通过使用块采样策略可以显着提高算法的效率。不幸的是，这种策略的直接实现是不可能的，因为它需要不允许封闭形式表达式的积分知识。本文介绍了一种新的方法，它绕过了这个问题，是标准SMC方法的自然扩展。应用于几个顺序贝叶斯推理问题证明了这些方法。
Sequential Monte Carlo (SMC) methods are a powerful set of simulation-based techniques for sampling sequentially from a sequence of complex probability distributions. These methods rely on a combination of importance sampling and resampling techniques. In a Markov chain Monte Carlo (MCMC) framework, block sampling strategies often perform much better than algorithms based on one-at-a-time sampling strategies if "good" proposal distributions to update blocks of variables can be designed. In an SMC framework, standard algorithms sequentially sample the variables one at a time whereas, like MCMC, the efficiency of algorithms could be improved significantly by using block sampling strategies. Unfortunately, a direct implementation of such strategies is impossible as it requires the knowledge of integrals which do not admit closed-form expressions. This article introduces a new methodology which bypasses this problem and is a natural extension of standard SMC methods. Applications to several sequential Bayesian inference problems demonstrate these methods.