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RESEARCH PRODUCT

Distributed Particle Metropolis-Hastings Schemes

Luca MartinoVictor ElviraGustau Camps-valls

subject

Computer scienceMonte Carlo methodErgodicity020206 networking & telecommunications02 engineering and technologyFilter (signal processing)Bayesian inferenceStatistics::ComputationSet (abstract data type)Metropolis–Hastings algorithm[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingTransmission (telecommunications)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingParticle filter[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmComputingMilieux_MISCELLANEOUS

description

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.

yearjournalcountryeditionlanguage
2018-06-102018 IEEE Statistical Signal Processing Workshop (SSP)
https://doi.org/10.1109/ssp.2018.8450763