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Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.

Arhit Chakrabarti1, Yang Ni2, Ellen Ruth A Morris3

  • 1Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.

Journal of Machine Learning Research : JMLR
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the graphical Dirichlet process for clustering non-exchangeable grouped data. This Bayesian approach enables cluster sharing across dependent groups, improving analysis of complex datasets.

Keywords:
Bayesian nonparametricsclusteringdirected acyclic graphfamily-owned restaurant processnon-exchangeable groups

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Area of Science:

  • Statistics
  • Computational Biology
  • Machine Learning

Background:

  • Clustering grouped data with non-exchangeable groups presents analytical challenges.
  • Existing methods often struggle to model complex dependencies between groups.

Purpose of the Study:

  • To propose a novel Bayesian nonparametric method for clustering grouped data with dependencies.
  • To enable sharing of clusters among non-exchangeable groups using a directed acyclic graph structure.

Main Methods:

  • Introduced the graphical Dirichlet process, a Bayesian nonparametric model.
  • Leveraged a directed acyclic graph to characterize dependencies between group-specific random measures.
  • Developed an efficient posterior inference algorithm for model estimation.

Main Results:

  • The graphical Dirichlet process jointly models dependent group-specific random measures.
  • The model respects the Markov property of the directed acyclic graph.
  • Demonstrated model utility through simulations and analysis of single-cell data.

Conclusions:

  • The graphical Dirichlet process offers a flexible framework for clustering complex grouped data.
  • The method effectively handles non-exchangeable groups and their dependencies.
  • Applicable to various fields, including bioinformatics and single-cell data analysis.