ISSN :2582-9793

On the properties of Gaussian Copula Mixture Models

Original Research (Published On: 18-Sep-2023 )
On the properties of Gaussian Copula Mixture Models
DOI : 10.54364/AAIML.2023.1184

ke wan and Alain Kornhauser

Adv. Artif. Intell. Mach. Learn., 3 (3):1444-1459

ke wan : PH.D

Alain Kornhauser : Princeton University

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DOI: 10.54364/AAIML.2023.1184

Article History: Received on: 05-May-23, Accepted on: 11-Sep-23, Published on: 18-Sep-23

Corresponding Author: ke wan

Email: kwan@alumni.princeton.edu

Citation: ke wan, Alain Kornhauser (2023). On the properties of Gaussian Copula Mixture Models. Adv. Artif. Intell. Mach. Learn., 3 (3 ):1444-1459


Abstract

    

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 component 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.

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