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Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

cam.issuedOnline2018-12-03
dc.contributor.authorHigson, E
dc.contributor.authorHandley, W
dc.contributor.authorHobson, M
dc.contributor.authorLasenby, A
dc.contributor.orcidHigson, E [0000-0001-8383-4614]
dc.date.accessioned2019-01-22T00:30:22Z
dc.date.available2019-01-22T00:30:22Z
dc.date.issued2019
dc.description.abstractWe introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of "live points" varies to allocate samples more efficiently. In empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same number of samples; this increase in accuracy is equivalent to speeding up the computation by factors of up to ~72 for parameter estimation and ~7 for evidence calculations. We also show that the accuracy of both parameter estimation and evidence calculations can be improved simultaneously. In addition, unlike in standard nested sampling, more accurate results can be obtained by continuing the calculation for longer. Popular standard nested sampling implementations can be easily adapted to perform dynamic nested sampling, and several dynamic nested sampling software packages are now publicly available.
dc.identifier.doi10.17863/CAM.35597
dc.identifier.eissn1573-1375
dc.identifier.issn0960-3174
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288281
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.publisher.urlhttp://dx.doi.org/10.1007/s11222-018-9844-0
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectNested sampling
dc.subjectParameter estimation
dc.subjectBayesian evidence
dc.subjectBayesian computation
dc.titleDynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
dc.typeArticle
prism.endingPage913
prism.issueIdentifier5
prism.publicationDate2019
prism.publicationNameStatistics and Computing
prism.startingPage891
prism.volume29
pubs.funder-project-idSTFC (1208121)
rioxxterms.licenseref.startdate2019-09-11
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1007/s11222-018-9844-0

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