  In data science, it is often required to estimate dependencies between
different data sources. These dependencies are typically calculated using
Pearson's correlation, distance correlation, and/or mutual information.
However, none of these measures satisfy all the Granger's axioms for an "ideal
measure". One such ideal measure, proposed by Granger himself, calculates the
Bhattacharyya distance between the joint probability density function (pdf) and
the product of marginal pdfs. We call this measure the mutual dependence.
However, to date this measure has not been directly computable from data. In
this paper, we use our recently introduced maximum likelihood non-parametric
estimator for band-limited pdfs, to compute the mutual dependence directly from
the data. We construct the estimator of mutual dependence and compare its
performance to standard measures (Pearson's and distance correlation) for
different known pdfs by computing convergence rates, computational complexity,
and the ability to capture nonlinear dependencies. Our mutual dependence
estimator requires fewer samples to converge to theoretical values, is faster
to compute, and captures more complex dependencies than standard measures.
