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dc.contributor.authorBacallado de Lara, SA
dc.contributor.authorBattiston, M
dc.contributor.authorFavaro, S
dc.contributor.authorTrippa, L
dc.date.accessioned2018-01-10T16:20:21Z
dc.date.available2018-01-10T16:20:21Z
dc.date.issued2017-11-01
dc.identifier.issn0883-4237
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/270479
dc.description.abstractA fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assuming that the corresponding predictive probabilities obey certain properties. An early discussion of such a problem, although in a parametric framework, dates back to the seminal work by English philosopher W. E. Johnson, who introduced a noteworthy characterization for the predictive probabilities of the symmetric Dirichlet prior distribution. This is typically referred to as Johnson’s “sufficientness” postulate. In this paper we review some nonparametric generalizations of Johnson’s postulate for a class of nonparametric priors known as species sampling models. In particular we revisit and discuss the “sufficientness” postulate for the two parameter Poisson-Dirichlet prior within the more general framework of Gibbs-type priors and their hierarchical generalizations.
dc.description.sponsorship. Stefano Favaro is supported by the European Research Council through StG N-BNP 306406. Marco Battiston’s research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC grant agreement number 617071.
dc.publisherInstitute of Mathematical Statistics
dc.subjectBayesian nonparametrics
dc.subjectDirichlet and two parameter Poisson–Dirichlet process
dc.subjectdiscovery probability
dc.subjectGibbs-type species sampling models
dc.subjecthierarchical species sampling models
dc.subjectJohnson’s “sufficientness” postulate
dc.subjectPólya-like urn scheme
dc.titleSufficientness postulates for Gibbs-type priors and hierarchical generalizations
dc.typeArticle
prism.endingPage500
prism.issueIdentifier4
prism.publicationNameStatistical Science
prism.startingPage487
prism.volume32
dc.identifier.doi10.17863/CAM.17361
dcterms.dateAccepted2017-06-10
rioxxterms.versionofrecord10.1214/17-STS619
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-06-10
dc.contributor.orcidBacallado de Lara, Sergio [0000-0002-7193-6450]
rioxxterms.typeJournal Article/Review
cam.issuedOnline2017-11-28
datacite.issupplementedby.doi10.1214/17-STS619SUPP
rioxxterms.freetoread.startdate2018-11-28


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