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A New Method for Tracking Configuration for Dirichlet Process Sampling

Authors:

Rui Wu,

Novartis Pharmaceuticals Corporation, East Hanover, New Jersey,, US
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Ming-Hui Chen ,

Department of Statistics, University of Connecticut, Connecticut,, US
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Lynn Kuo,

Department of Statistics, University of Connecticut, Connecticut,, US
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Paul O. Lewis

Department of Ecology and Evolutionary Biology, University of Connecticut, Connecticut,, US
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Abstract

The method of fitting a hierarchical model with Dirichlet process mixing is a versatile tool for data analysts. It has been applied to density estimation, classification, clustering, and high dimensional data analysis. Many computing algorithms have been proposed to evaluate this mixture. Different labels in the algorithm that assign data points into clusters may actually yield the same partition configuration. This paper makes this notion rigorous by establishing an equivalence theorem. Thus, we would recommend adding the step of checking for equivalent configurations to the algorithms for evaluating hierarchical Dirichlet process mixing models for improved results, especially when cluster assignments are the major goals of the analysis.

DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7781

How to Cite: Wu, R. et al., (2014). A New Method for Tracking Configuration for Dirichlet Process Sampling. Sri Lankan Journal of Applied Statistics. 5(4), pp.1–16. DOI: http://doi.org/10.4038/sljastats.v5i4.7781
Published on 14 Dec 2014.
Peer Reviewed

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