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Cluster Sampling Filters for Non-Gaussian Data Assimilation

dc.contributor.authorAttia, A.en
dc.contributor.authorMoosavi, Azamen
dc.contributor.authorSandu, Adrianen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2017-03-06T18:31:25Zen
dc.date.available2017-03-06T18:31:25Zen
dc.date.issued2016-08-19en
dc.description.abstractThis paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a Gaussian Mixture Model (GMM) to the prior ensemble. Using the data likelihood function, the posterior density is then formulated as a mixture density, and is sampled using a HMC approach (or any other scheme capable of sampling multimodal densities in high-dimensional subspaces). The main filter developed herein is named "cluster HMC sampling filter" (ClHMC). A multi-chain version of the ClHMC filter, namely MC-ClHMC is also proposed to guarantee that samples are taken from the vicinities of all probability modes of the formulated posterior. The new methodologies are tested using a quasi-geostrophic (QG) model with double-gyre wind forcing and bi-harmonic friction. Numerical results demonstrate the usefulness of using GMMs to relax the Gaussian prior assumption in the HMC filtering paradigm.en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/75260en
dc.language.isoenen
dc.relation.urihttp://arxiv.org/abs/1607.03592v2en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectstat.COen
dc.subjectcs.NAen
dc.subjectstat.APen
dc.titleCluster Sampling Filters for Non-Gaussian Data Assimilationen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen

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