Miscellanea: Generalized Ewens-Pitman model for Bayesian clustering

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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.

Original languageEnglish (US)
Pages (from-to)231-238
Number of pages8
JournalBiometrika
Volume102
Issue number1
DOIs
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Clustering
  • Discrete parameter
  • Ewens-Pitman distribution
  • Partition data
  • Random partition

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