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dc.contributor.authorHainy, Markus
dc.contributor.authorPrice, David
dc.contributor.authorRestif, Olivier
dc.contributor.authorDrovandi, Christopher
dc.date.accessioned2022-04-07T23:30:22Z
dc.date.available2022-04-07T23:30:22Z
dc.date.issued2022-02-22
dc.identifier.issn0960-3174
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335896
dc.description.abstractPerforming optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from the candidate models than previous approaches using approximate Bayesian computation. Further, it is easy to assess the performance of the optimal design through the misclassification error rate. The approach is particularly useful in the presence of models with intractable likelihoods but can also provide computational advantages when the likelihoods are manageable.
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council grant BB/M020193/1
dc.publisherSpringer
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleOptimal Bayesian design for model discrimination via classification
dc.typeArticle
dc.publisher.departmentDepartment of Veterinary Medicine
dc.date.updated2022-04-07T12:02:01Z
prism.publicationDate2022
prism.publicationNameStatistics and Computing
dc.identifier.doi10.17863/CAM.83330
dcterms.dateAccepted2022-01-20
rioxxterms.versionofrecord10.1007/s11222-022-10078-2
rioxxterms.versionVoR
dc.contributor.orcidRestif, Olivier [0000-0001-9158-853X]
rioxxterms.typeJournal Article/Review
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (BB/M020193/1)
cam.issuedOnline2022-02-22
cam.depositDate2022-04-07
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