Copula Density Neural Estimation

TL;DR

This paper introduces CODINE, a neural network-based method for estimating copula densities, enabling modeling of complex dependencies between variables, with applications in mutual information estimation and data generation.

Contribution

The paper presents a novel neural network approach for copula density estimation, effectively capturing complex dependence structures in data.

Findings

Capable of modeling complex distributions

Applicable to mutual information estimation

Useful for data generation

Abstract

Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.