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Generalized Ewens–Pitman model for Bayesian clustering

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  • Harry Crane

Abstract

We propose a Bayesian method for clustering from discrete data structures that commonly arise in genetics and other applications. This method is equivariant with respect to relabelling units; unsampled units do not interfere with sampled data; and missing data do not hinder inference. Cluster inference using the posterior mode performs well on simulated and real datasets, and the posterior predictive distribution enables supervised learning based on a partial clustering of the sample.

Suggested Citation

  • Harry Crane, 2015. "Generalized Ewens–Pitman model for Bayesian clustering," Biometrika, Biometrika Trust, vol. 102(1), pages 231-238.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:231-238.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu052
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