Theory and computations for the dirichlet process and related models: an overview

International Journal of Approximate Reasoning

dc.creatorJara-Vallejos, Alejandro Antonio
dc.date2018-11-29T15:36:47Z
dc.date2022-07-07T15:53:52Z
dc.date2014
dc.date2018-11-29T15:36:47Z
dc.date2022-07-07T15:53:52Z
dc.date2017
dc.date.accessioned2023-08-21T22:50:01Z
dc.date.available2023-08-21T22:50:01Z
dc.identifier1141193
dc.identifier1141193
dc.identifierhttps://hdl.handle.net/10533/228506
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8292051
dc.descriptionData analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distributi
dc.descriptionRegular
dc.descriptionFONDECYT
dc.descriptionFONDECYT
dc.languageeng
dc.relationhandle/10533/111556
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.relationhttps://www.sciencedirect.com/science/article/pii/S0888613X16302328
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.titleTheory and computations for the dirichlet process and related models: an overview
dc.titleInternational Journal of Approximate Reasoning
dc.typeArticulo
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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