Dependent Modeling of Temporal Sequences of Random Partitions

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dc.contributor.authorPage, Garritt L.
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorDahl, David B.
dc.date.accessioned2025-01-20T22:01:30Z
dc.date.available2025-01-20T22:01:30Z
dc.date.issued2022
dc.description.abstractWe consider modeling a dependent sequence of random partitions. It is well known in Bayesian non-parametrics that a random measure of discrete type induces a distribution over random partitions. The community has therefore assumed that the best approach to obtain a dependent sequence of random partitions is through modeling dependent random measures. We argue that this approach is problematic and show that the random partition model induced by dependent Bayesian nonparametric priors exhibits counter-intuitive dependence among partitions even though the dependence for the sequence of random probability measures is intuitive. Because of this, we suggest directly modeling the sequence of random partitions when clustering is of principal interest. To this end, we develop a class of dependent random partition models that explicitly models dependence in a sequence of partitions. We derive conditional and marginal properties of the joint partition model and devise computational strategies when employing the method in Bayesian modeling. In the case of temporal dependence, we demonstrate through simulation how the methodology produces partitions that evolve gently and naturally overtime. We further illustrate the utility of the method by applying it to an environmental dataset that exhibits spatio-temporal dependence. Supplemental files for this article are available online.
dc.fuente.origenWOS
dc.identifier.doi10.1080/10618600.2021.1987255
dc.identifier.eissn1537-2715
dc.identifier.issn1061-8600
dc.identifier.urihttps://doi.org/10.1080/10618600.2021.1987255
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93839
dc.identifier.wosidWOS:000722808600001
dc.issue.numero2
dc.language.isoen
dc.pagina.final627
dc.pagina.inicio614
dc.revistaJournal of computational and graphical statistics
dc.rightsacceso restringido
dc.subjectBayesian nonparametrics
dc.subjectCorrelated partitions
dc.subjectHierarchical Bayes modeling
dc.subjectSpatio-temporal clustering
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleDependent Modeling of Temporal Sequences of Random Partitions
dc.typeartículo
dc.volumen31
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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