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December 2010 Inference of global clusters from locally distributed data
XuanLong Nguyen
Bayesian Anal. 5(4): 817-845 (December 2010). DOI: 10.1214/10-BA529

Abstract

We consider the problem of analyzing the heterogeneity of clustering distributions for multiple groups of observed data, each of which is indexed by a covariate value, and inferring global clusters arising from observations aggregated over the covariate domain. We propose a novel Bayesian nonparametric method reposing on the formalism of spatial modeling and a nested hierarchy of Dirichlet processes. We provide an analysis of the model properties, relating and contrasting the notions of local and global clusters. We also provide an efficient inference algorithm, and demonstrate the utility of our method in several data examples, including the problem of object tracking and a global clustering analysis of functional data where the functional identity information is not available.

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XuanLong Nguyen. "Inference of global clusters from locally distributed data." Bayesian Anal. 5 (4) 817 - 845, December 2010. https://doi.org/10.1214/10-BA529

Information

Published: December 2010
First available in Project Euclid: 19 June 2012

zbMATH: 1330.62255
MathSciNet: MR2740158
Digital Object Identifier: 10.1214/10-BA529

Keywords: Gaussian process , global clustering , Graphical model , Hierarchical Dirichlet process , local clustering , Markov chain Monte Carlo , model identifiability , nonparametric Bayes , spatial dependence

Rights: Copyright © 2010 International Society for Bayesian Analysis

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Vol.5 • No. 4 • December 2010
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