Copula Density Neural Estimation

Understand

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.