We consider the problem of analyzing the heterogeneity of cluster- ing distributions for multiple groups of observed data, each of which is indexed by a covariate value, and inferring global clusters arising from observations ag- gregated 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 ex- amples, including the problem of object tracking and a global clustering analysis of functional data where the functional identity information is not available.
['XuanLong Nguyen']
Inference of global clusters from locally distributed data