Modeling with Normalized Random Measure Mixture Models

The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in no...

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Veröffentlicht in:	Statistical science 2013-08, Vol.28 (3), p.313-334
Hauptverfasser:	Barrios, Ernesto, Lijoi, Antonio, Nieto-Barajas, Luis E., Prünster, Igor
Format:	Artikel
Sprache:	eng
Schlagworte:	A priori knowledge Barrios Bayesian nonparametrics clustering completely random measure Datasets Density Density estimation Dirichlet problem Dirichlet process Estimators increasing additive process latent variables Mathematical models mixture model Modeling Nonparametric models normalized generalized gamma process normalized inverse Gaussian process normalized random measure normalized stable process Random variables Simulations Statistical analysis
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