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Measuring and quantifying dependencies between random variables (RV's) cangive critical insights into a data-set. Typical questions are: `Do underlyingrelationships exist?', `Are some variables redundant?', and `Is some targetvariable $Y$ highly or weakly dependent on variable $X$?' Interestingly,despite the evident need for a general-purpose measure of dependency betweenRV's, common practice of data analysis is that most data analysts use thePearson correlation coefficient (PCC) to quantify dependence between RV's,while it is well-recognized that the PCC is essentially a measure for lineardependency only. Although many attempts have been made to define more genericdependency measures, there is yet no consensus on a standard, general-purposedependency function. In fact, several ideal properties of a dependency functionhave been proposed, but without much argumentation. Motivated by this, in thispaper we will discuss and revise the list of desired properties and propose anew dependency function that meets all these requirements. This general-purposedependency function provides data analysts a powerful means to quantify thelevel of dependence between variables. To this end, we also provide Python codeto determine the dependency function for use in practice.

The BP Dependency Function: a Generic Measure of Dependence between Random Variables