A Test for Independence Via Bayesian Nonparametric Estimation of Mutual Information
Methodology
2021-08-10 v2 Computation
Abstract
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 -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.
Cite
@article{arxiv.2002.03490,
title = {A Test for Independence Via Bayesian Nonparametric Estimation of Mutual Information},
author = {Luai Al-Labadi and Forough Fazeli Asl and Zahra Saberi},
journal= {arXiv preprint arXiv:2002.03490},
year = {2021}
}
Comments
32 pages