On the Properties of Gaussian Copula Mixture Models

Research output: Contribution to journalArticlepeer-review


This paper investigates Gaussian copula mixture models (GCMM), which are an extension of Gaussian mixture models (GMM) that incorporate copula concepts. The paper presents the mathematical definition of GCMM and explores the properties of its likelihood function. Additionally, the paper proposes extended Expectation Maximum algorithms to estimate parameters for the mixture of copulas; the marginal distributions corresponding to each com-ponent are estimated separately using non-parametric statistical methods. In the experiment, GCMM demonstrates improved goodness-of-fitting compared to GMM when using the same number of clusters. Furthermore, GCMM has the ability to leverage un-synchronized data across dimensions for more comprehensive data analysis.

Original languageEnglish (US)
Pages (from-to)1444-1459
Number of pages16
JournalAdvances in Artificial Intelligence and Machine Learning
Issue number3
StatePublished - 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


  • Copula
  • Gaussian Copula Mixture Models (GCMM)
  • Gaussian mixture
  • Gaussian processes
  • Kernels
  • Machine learning
  • Model clustering


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