DISTRIBUTED PARTICLE METROPOLIS-HASTINGS SCHEMES

被引：0

作者：

Martino, Luca ^{[1
]}

Elvira, Victor ^{[2
]}

Camps-Valls, Gustau ^{[1
]}

机构：

[1] Univ Valencia, Image Proc Lab, Valencia, Spain

[2] IMT Lille Douai CRISTAL, UMR 9189, Villeneuve Dascq, France

来源：

2018 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2018年

基金：

欧洲研究理事会;

关键词：

Particle MCMC; Particle Filtering; Monte Carlo; Bayesian inference; state-space models; RESAMPLING ALGORITHMS; SELECTION; TRACKING;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：553 / 557

页数：5

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