A short tutorial on bayesian nonparametrics

Journal of Statistical Research

dc.creatorMuller, Peter
dc.creatorXu, Yanxun
dc.creatorJara-Vallejos, Alejandro Antonio
dc.date2018-11-29T15:36:47Z
dc.date2022-07-07T15:53:57Z
dc.date2014
dc.date2018-11-29T15:36:47Z
dc.date2022-07-07T15:53:57Z
dc.date2016
dc.date.accessioned2023-08-22T23:27:10Z
dc.date.available2023-08-22T23:27:10Z
dc.identifier1141193
dc.identifier1141193
dc.identifierhttps://hdl.handle.net/10533/228508
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8351191
dc.descriptionBayesian nonparametric (BNP) models are prior models for infinite-dimensional parameters, such as an unknown probability measure F or an unknown regression mean function f. We review some of the most widely used BNP priors, including the Dirichlet process
dc.descriptionRegular
dc.descriptionFONDECYT
dc.descriptionFONDECYT
dc.languageeng
dc.relationhandle/10533/111556
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.relationhttp://jsr.isrt.ac.bd/wp-content/uploads/48_50n2_1.pdf
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleA short tutorial on bayesian nonparametrics
dc.titleJournal of Statistical Research
dc.typeArticulo
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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