Decision tree-based estimation of the overlap of two probability distributions

TL;DR

This paper introduces a decision tree-based nonparametric method to estimate the overlap between two probability distributions, providing analytical and numerical validation along with convergence proofs.

Contribution

It presents a novel decision tree algorithm for estimating distribution overlap, with theoretical convergence guarantees and experimental validation.

Findings

The method accurately estimates distribution overlap.

Convergence to true overlap is theoretically proven.

Experimental results demonstrate effectiveness.

Abstract

A new nonparametric approach, based on a decision tree algorithm, is proposed to calculate the overlap between two probability distributions. The devised framework is described analytically and numerically. The convergence of the estimated overlap to the true value is proved along with some experimental results.