Forest resampling for distributed sequential Monte Carlo

Forest resampling for distributed sequential Monte Carlo
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分布式顺序蒙特卡罗的森林重采样

DOI:
10.1002/sam.11280
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发表时间:
2015
期刊:
The ASA Data Science Journal
影响因子:
--
通讯作者:
Lee A
Lee A
中科院分区:
--
文献类型:
--
作者:
Lee A

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本文将分布式计算体系结构和数据结构的明确考虑引入到序贯蒙特卡罗(SMC)方法的严格设计中。作者最近建立的一个理论结果表明,适应粒子之间的相互作用,以适当地控制有效样本大小(ESS)是足以保证SMC算法的稳定性。我们的目标是利用这一结果,并设计算法,从而保证在分布式环境中工作良好。我们为此作出三项主要贡献。首先,我们研究ESS的数学性质作为一个函数的矩阵和图形,参数化粒子之间的相互作用。其次,我们展示了这些图可以诱导树数据结构模型的逻辑网络拓扑结构的抽象分布式计算环境。最后,我们提出了高效的分布式算法,实现所需的ESS控制,执行rescovery和操作森林与这些树。© 2015威利期刊公司.统计分析和数据挖掘:阿萨数据科学杂志,2015年
This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the effective sample size (ESS) is sufficient to guarantee stability of SMC algorithms. Our objective is to leverage this result and devise algorithms which are thus guaranteed to work well in a distributed setting. We make three main contributions to achieve this. First, we study mathematical properties of the ESS as a function of matrices and graphs that parameterize the interaction among particles. Secondly, we show how these graphs can be induced by tree data structures which model the logical network topology of an abstract distributed computing environment. Finally, we present efficient distributed algorithms that achieve the desired ESS control, perform resampling and operate on forests associated with these trees. © 2015 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2015
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