On the Multimodality of Random Probability Measures

Kokolakis, G; Kouvaras, G

dc.contributor.author	Kokolakis, G	en
dc.contributor.author	Kouvaras, G	en
dc.date.accessioned	2014-03-01T01:56:31Z
dc.date.available	2014-03-01T01:56:31Z
dc.date.issued	2007	en
dc.identifier.issn	1931-6690	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28138
dc.subject	convexity	en
dc.subject	Dirichlet process	en
dc.subject	multimodal distribution functions	en
dc.subject	Polya trees	en
dc.subject	random probability measures	en
dc.title	On the Multimodality of Random Probability Measures	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Nonparametric methods for density estimation are examined here. Within a Bayesian setting the construction of an absolutely continuous random probability measure is often required for nonparametric statistical analysis. To achieve this we propose a "partial convexification" procedure of a process, such as the Dirichlet, resulting in a multimodal distribution function with a finite expected number of modes. In agreement with convexity theory results, it is shown that the derived random probability measure admits a density with respect to Lebesgue measure.	en
heal.publisher	INT SOC BAYESIAN ANALYSIS	en
heal.journalName	BAYESIAN ANALYSIS	en
dc.identifier.isi	ISI:000207454400011	en
dc.identifier.volume	2	en
dc.identifier.issue	1	en
dc.identifier.spage	213	en
dc.identifier.epage	219	en