Bayesian nonparametric estimators derived from conditional Gibbs structures

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dc.creatorLijoi, Antonio
dc.creatorPrünster, Igor
dc.creatorWalker, Stephen G.
dc.date2008-08-21
dc.date.accessioned2026-07-07T09:57:39Z
dc.date.available2026-07-07T09:57:39Z
dc.descriptionWe consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail. In particular, we deduce conditional distributions and the corresponding Bayesian nonparametric estimators, which can be readily exploited for predicting various features of additional samples. The results provide useful tools for genomic applications where prediction of future outcomes is required.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AAP495 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0808.2863
dc.identifierhttp://arxiv.org/abs/0808.2863
dc.identifierAnnals of Applied Probability 2008, Vol. 18, No. 4, 1519-1547
dc.identifierdoi:10.1214/07-AAP495
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/167424
dc.subjectProbability
dc.subject62G05, 62F15, 60G57 (Primary)
dc.titleBayesian nonparametric estimators derived from conditional Gibbs structures
dc.typetext

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