An inferential measure of dependence between two systems using Bayesian model comparison

TL;DR

This paper introduces a Bayesian model comparison approach to measure dependence between two systems, providing a probabilistic quantification of the evidence for dependence based on observed data.

Contribution

It proposes a novel Bayesian dependence measure, $B(X,Y|D)$, that quantifies the evidence for dependence as a posterior probability, and explores its properties and advantages over traditional measures.

Findings

$B(X,Y|D)$ quantifies dependence as a posterior probability.

The measure is robust to noise and sensitive to dependence intensity.

It shares similarities with mutual information but offers a Bayesian interpretability.

Abstract

We propose to quantify dependence between two systems $X$ and $Y$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_0$, of statistical independence and another one, $H_1$, of dependence. In this framework, dependence between $X$ and $Y$ in $D$, denoted $B(X,Y|D)$, is quantified as $P(H_1|D)$, the posterior probability for the model of dependence given $D$, or any strictly increasing function thereof. It is therefore a measure of the evidence for dependence between $X$ and $Y$ as modeled by $H_1$ and observed in $D$. We review several statistical models and reconsider standard results in the light of $B(X,Y|D)$ as a measure of dependence. Using simulations, we focus on two specific issues: the effect of noise and the behavior of $B(X,Y|D)$ when $H_1$ has a parameter coding for the intensity of dependence. We then derive some general properties of $B(X,Y|D)$, showing that it quantifies the information contained in $D$ in favor of $H_1$ versus $H_0$. While some of these properties are typical of what is expected from a valid measure of dependence, others are novel and naturally appear as desired features for specific measures of dependence, which we call inferential. We finally put these results in perspective; in particular, we discuss the consequences of using the Bayesian framework as well as the similarities and differences between $B(X,Y|D)$ and mutual information.