Bayesian Analysis

On the multimodality of random probability measures

George Kokolakis and George Kouvaras

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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.

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Bayesian Anal., Volume 2, Number 1 (2007), 213-219.

First available in Project Euclid: 22 June 2012

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convexity Dirichlet process multimodal distribution functions Polya trees random probability measures


Kokolakis, George; Kouvaras, George. On the multimodality of random probability measures. Bayesian Anal. 2 (2007), no. 1, 213--219. doi:10.1214/07-BA208.

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