分布粒子大都会-黑斯廷斯方案

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI:10.1109/SSP.2018.8450763

Luca Martino, V. Elvira, Gustau Camps-Valls

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引用次数: 10

摘要

提出了一种由多个并行粒子滤波器驱动的粒子Metropolis-Hastings算法。与中心节点的通信只需要传输一组加权样本，每个滤波器一个。在此基础上，提出了一种边缘版本的分布式粒子边缘Metropolis-Hastings (DPMMH)方法。DPMMH可用于对感兴趣的动态和静态变量进行推断。遍历性得到了保证，数值仿真结果表明了该方案的优越性。

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Distributed Particle Metropolis-Hastings Schemes

We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called Distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.

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来源期刊

2018 IEEE Statistical Signal Processing Workshop (SSP)

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