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A note on the finite-dimensional Dirichlet prior

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  • Xia Yemao
  • Gou Jianwei

Abstract

As an approximation to the Dirichlet process which involves the infinite-dimensional distribution, finite-dimensional Dirichlet prior is a widely appreciated method to model the underlying distribution in non parametric Bayesian analysis. In this short note, we present some key characteristics of finite-dimensional Dirichlet process and exploit some important sampling properties which are very useful in Bayesian non parametric/semiparametric analysis.

Suggested Citation

  • Xia Yemao & Gou Jianwei, 2017. "A note on the finite-dimensional Dirichlet prior," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(19), pages 9388-9396, October.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:19:p:9388-9396
    DOI: 10.1080/03610926.2016.1208239
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