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dc.creatorAiroldi, EM
dc.creatorCosta, T
dc.creatorBassetti, F
dc.creatorLeisen, F
dc.creatorGuindani, M
dc.date.accessioned2021-02-03T18:38:27Z
dc.date.available2021-02-03T18:38:27Z
dc.date.issued2014-10-02
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5837
dc.identifier.otherAX2TI (isidoc)
dc.identifier.other25870462 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5855
dc.description.abstract© 2014, © 2014 American Statistical Association. Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of nonexchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet process and the two parameters Poisson–Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes’ modeling framework, and we describe a Markov chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet process mixtures and hidden Markov models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array comparative genomic hybridization (CGH) data. Supplementary materials for this article are available online.
dc.format.extent1466-1480
dc.language.isoen
dc.relation.haspartJournal of the American Statistical Association
dc.relation.isreferencedbyInforma UK Limited
dc.subjectBayesian nonparametrics
dc.subjectCancer
dc.subjectGenomics
dc.subjectMCMC
dc.subjectPredictive probability functions
dc.subjectRandom partitions
dc.titleGeneralized Species Sampling Priors With Latent Beta Reinforcements
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1080/01621459.2014.950735
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidAiroldi, Edoardo|0000-0002-3512-0542
dc.date.updated2021-02-03T18:38:24Z
refterms.dateFOA2021-02-03T18:38:28Z


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