dc.date.accessioned | 2013-03-12T08:18:11Z | |
dc.date.available | 2013-03-12T08:18:11Z | |
dc.date.issued | 2003 | en_US |
dc.date.submitted | 2011-07-08 | en_US |
dc.identifier.uri | http://hdl.handle.net/10852/10312 | |
dc.description.abstract | A family of nonparametric prior distributions which extends the Dirichlet process is introduced and studied. Such family is first constructed by normalising suitable compound Poisson processes. An alternative derivation shows that such priors admit a simple representation as discrete random probability measures with symmetric Dirichlet weights independent of i.i.d. locations. The latter representation proves useful in deriving manageable expressions for the posterior and predictive distributions. A number of Bayesian nonparametric estimators based on the family are discussed. Furthermore, an analysis of the characteristics of a sample drawn from the family demonstrates its potential as a second stage prior in hierarchical Bayesian clustering models. | eng |
dc.language.iso | eng | en_US |
dc.publisher | Matematisk Institutt, Universitetet i Oslo | |
dc.relation.ispartof | Preprint series. Statistical Research Report http://urn.nb.no/URN:NBN:no-23420 | en_US |
dc.relation.uri | http://urn.nb.no/URN:NBN:no-23420 | |
dc.rights | © The Author(s) (2003). This material is protected by copyright law. Without explicit authorisation, reproduction is only allowed in so far as it is permitted by law or by agreement with a collecting society. | |
dc.title | Bayesian Inference Using an Extension of the Dirichlet Process | en_US |
dc.type | Research report | en_US |
dc.date.updated | 2011-07-08 | en_US |
dc.rights.holder | Copyright 2003 The Author(s) | |
dc.creator.author | Hjort, Nils Lid | en_US |
dc.creator.author | Ongaro, Andrea | en_US |
dc.subject.nsi | VDP::410 | en_US |
dc.identifier.urn | URN:NBN:no-28257 | en_US |
dc.type.document | Forskningsrapport | en_US |
dc.identifier.duo | 132105 | en_US |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/10312/1/stat-res-11-03.pdf | |