# A Bayesian Semiparametric Gaussian Copula Approach to a Multivariate Normality Test

arXiv: https://arxiv.org/abs/1907.01736 · HTML: https://mlsanity.com/paper/1907.01736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks](https://mlsanity.com/paper/2303.02637/) (2023)

## Abstract (arXiv)

In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution Ftrue. First, the Dirichlet process is constructed on the unknown marginal distributions of Ftrue. Then a Gaussian copula model is utilized to capture the dependence structure of Ftrue. As a result, a Bayesian multivariate normality test is developed by combining the relative belief ratio and the Energy distance. Several interesting theoretical results of the approach are derived. Finally, through several simulated examples and a real data set, the proposed approach reveals excellent performance.