Posterior sampling from ε-approximation of normalized completely random measure mixtures

Raffaele Argiento, Ilaria Bianchini, Alessandra Guglielmi

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

4 Citazioni (Scopus)


Abstract: This paper adopts a Bayesian nonparametric mixture model where the mixing distribution belongs to the wide class of normalized homogeneous completely random measures. We propose a truncation method for the mixing distribution by discarding the weights of the unnormalized measure smaller than a threshold. We prove convergence in law of our approximation, provide some theoretical properties, and characterize its posterior distribution so that a blocked Gibbs sampler is devised. The versatility of the approximation is illustrated by two different applications. In the first the normalized Bessel random measure, encompassing the Dirichlet process, is introduced; goodness of fit indexes show its good performances as mixing measure for density estimation. The second describes how to incorporate covariates in the support of the normalized measure, leading to a linear dependent model for regression and clustering.
Lingua originaleEnglish
pagine (da-a)3516-3547
Numero di pagine32
RivistaElectronic Journal of Statistics
Stato di pubblicazionePubblicato - 2016


  • Bayesian nonparametric mixture models, normalized completely random measures, blocked Gibbs sampler, finite dimensional approximation


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