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Distributed Particle Metropolis-Hastings Schemes

Authors: L. Martino, V. Elvira, G. Camps-Valls

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.

Comments: 5 Pages.

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[v1] 2020-12-05 11:25:51

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