Models beyond the Dirichlet process

Antonio Lijoi and Igor Prünster

No 129, Carlo Alberto Notebooks from Collegio Carlo Alberto

Abstract: Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of flexibility it ensures in statistical modelling, if compared to parametric alternatives, and by the emergence of new and efficient simulation techniques that make nonparametric models amenable to concrete use in a number of applied statistical problems. Since its introduction in 1973 by T.S. Ferguson, the Dirichlet process has emerged as a cornerstone in Bayesian nonparametrics. Nonetheless, in some cases of interest for statistical applications the Dirichlet process is not an adequate prior choice and alternative nonparametric models need to be devised. In this paper we provide a review of Bayesian nonparametric models that go beyond the Dirichlet process.

Pages: 47 pages
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.carloalberto.org/wp-content/uploads/2018/11/no.129.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cca:wpaper:129

Access Statistics for this paper

More papers in Carlo Alberto Notebooks from Collegio Carlo Alberto Contact information at EDIRC.
Bibliographic data for series maintained by Giovanni Bert ().