Posterior convergence rate of a class of dirichlet process mixture model for compositional data

dc.date.accessioned2018-11-29T15:36:47Z
dc.date.accessioned2022-10-18T21:27:29Z
dc.date.available2018-11-29T15:36:47Z
dc.date.available2022-10-18T21:27:29Z
dc.date.created2018-11-29T15:36:47Z
dc.date.issued2017
dc.identifierhttp://hdl.handle.net/10533/228505
dc.identifier1141193
dc.identifierWOS:000388781800006
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4459861
dc.description.abstractWe propose a Dirichlet process mixture of mixtures of Dirichlet models for density estimation. By assuming random sampling from a density belonging to a Holder class, we show that the posterior distribution of the model is rate-optimal. (C) 2016 Elsevier
dc.languageeng
dc.relationhttps://www.sciencedirect.com/science/article/pii/S0167715215300730
dc.relationhandle/10533/111556
dc.relation10.1016/j.spl.2016.09.008
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.titlePosterior convergence rate of a class of dirichlet process mixture model for compositional data
dc.typeArticulo


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