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dc.contributor.authorDuffield, S
dc.contributor.authorSingh, SS
dc.date.accessioned2022-04-25T23:30:26Z
dc.date.available2022-04-25T23:30:26Z
dc.date.issued2022
dc.identifier.issn0167-7152
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336434
dc.description.abstractIn this letter we generalise Ensemble Kalman inversion techniques to general Bayesian models where previously they were restricted to additive Gaussian likelihoods - all in the difficult setting where the likelihood can be sampled from, but its density not necessarily evaluated.
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEnsemble Kalman inversion for general likelihoods
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-04-25T13:48:53Z
prism.publicationNameStatistics and Probability Letters
dc.identifier.doi10.17863/CAM.83851
dcterms.dateAccepted2022-04-24
rioxxterms.versionofrecord10.1016/j.spl.2022.109523
rioxxterms.versionVoR
dc.contributor.orcidDuffield, S [0000-0002-8656-8734]
dc.identifier.eissn1879-2103
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (1890282)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/M508007/1)
cam.issuedOnline2022-05-07
cam.orpheus.successWed Jun 08 08:57:14 BST 2022 - Embargo updated
cam.orpheus.success2022-06-08: VoR added to Apollo record
cam.orpheus.counter1
cam.depositDate2022-04-25
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International