Hierarchical Dirichlet processes and their applications: a survey

Jian Ying Zhou, Fei Yue Wang, Da Jun Zeng

Research output: Contribution to journalReview article

22 Scopus citations

Abstract

Dirichlet processes are a type of stochastic processes widely used in nonparametric Bayesian models, especially in research that involves probabilistic graphical models. Over the past few years, significant effort has been made in the study of such processes, mainly due to their modeling flexibility and wide applicability. For instance, Dirichlet processes are capable of learning the number of clusters as well as the corresponding parameters of each cluster whereas other clustering or classification models usually are not able to. In this survey, we first introduce the definitions of Dirichlet processes. We then present Dirichlet process mixture models and their applications, and discuss in detail hierarchical Dirichlet processes (HDP), their roles in constructing other models, and examples of related applications in many important fields. Finally, we summarize recent developments in the study and applications of hierarchical Dirichlet processes and offer our remarks on future research.

Original languageEnglish (US)
Pages (from-to)389-407
Number of pages19
JournalZidonghua Xuebao/Acta Automatica Sinica
Volume37
Issue number4
DOIs
StatePublished - Apr 1 2011
Externally publishedYes

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Keywords

  • Clustering
  • Dirichlet processes
  • Hierarchical Dirichlet processes (HDP)
  • Probabilistic graphical models

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Computer Graphics and Computer-Aided Design

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