# A Test for Independence Via Bayesian Nonparametric Estimation of Mutual Information

arXiv: https://arxiv.org/abs/2002.03490 · HTML: https://mlsanity.com/paper/2002.03490/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [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)
- [A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation](https://mlsanity.com/paper/2503.08902/) (2025)

## Abstract (arXiv)

Mutual information is a well-known tool to measure the mutual dependence between variables. In this paper, a Bayesian nonparametric estimation of mutual information is established by means of the Dirichlet process and the k-nearest neighbor distance. As a direct outcome of the estimation, an easy-to-implement test of independence is introduced through the relative belief ratio. Several theoretical properties of the approach are presented. The procedure is investigated through various examples where the results are compared to its frequentist counterpart and demonstrate a good performance.