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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/114995
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dc.contributor.authorPrice, D.-
dc.contributor.authorBean, N.-
dc.contributor.authorRoss, J.-
dc.contributor.authorTuke, J.-
dc.date.issued2018-
dc.identifier.citationComputational Statistics and Data Analysis, 2018; 126(C):112-124-
dc.identifier.issn0167-9473-
dc.identifier.issn1872-7352-
dc.identifier.urihttp://hdl.handle.net/2440/114995-
dc.description.abstractAbstract not avialable-
dc.description.statementofresponsibilityDavid J. Price, Nigel G. Bean, Joshua V. Ross, Jonathan Tuke-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2018 Elsevier B.V. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.csda.2018.04.011-
dc.subjectBayesian optimal design; optimisation heuristic; stochastic models; sampling windows-
dc.titleAn induced natural selection heuristic for finding optimal Bayesian experimental designs-
dc.typeJournal article-
dc.identifier.doi10.1016/j.csda.2018.04.011-
dc.relation.granthttp://purl.org/au-research/grants/arc/FT130100254-
pubs.publication-statusPublished-
dc.identifier.orcidBean, N. [0000-0002-5351-3104]-
dc.identifier.orcidRoss, J. [0000-0002-9918-8167]-
dc.identifier.orcidTuke, J. [0000-0002-1688-8951]-
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Mathematical Sciences publications

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