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This work aims to design a Gibbs sampling algorithm for posterior Bayesian inference of a Dirichlet process mixture model based on Hamming distributed kernels, a probability measure built upon the Hamming distance. This model is employed to provide model-based clustering analysis of categorical data with no natural ordering. The proposed algorithm leverages a split-and-merge Markov chain Monte Carlo technique to address the curse of dimensionality issue and improve the search over the space of random partitions.

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Split-and-Merge Sampling Algorithm for Hamming-Mixture Models of Categorical Data

  • 会议论文

  • 发表日期：

    2025年

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  • Sara Di Marino,
  • Filippo Galli,
  • Raffaele Argiento,
  • Andrea Cremaschi,
  • Lucia Paci

摘要

This work aims to design a Gibbs sampling algorithm for posterior Bayesian inference of a Dirichlet process mixture model based on Hamming distributed kernels, a probability measure built upon the Hamming distance. This model is employed to provide model-based clustering analysis of categorical data with no natural ordering. The proposed algorithm leverages a split-and-merge Markov chain Monte Carlo technique to address the curse of dimensionality issue and improve the search over the space of random partitions.