Copula Density Neural Estimation
This work addresses a central task in statistics for data analysis, but appears incremental as it applies neural networks to an existing copula estimation framework.
The paper tackled the problem of estimating copula densities to describe dependence between random variables, using a neural network-based method called CODINE, which showed capability in modeling complex distributions for tasks like mutual information estimation and data generation.
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.