Vine Copula

Vine copulas are a powerful tool for modeling complex multivariate probability distributions by decomposing them into simpler bivariate relationships. Current research focuses on improving their efficiency and applicability, particularly through integration with other models like quasi-Bayesian methods and deep neural networks to create hybrid architectures such as Vine-Copula Neural Networks (VCNNs). This allows for more accurate and robust uncertainty quantification in various applications, including high-dimensional density estimation and improved calibration of deep learning predictions. The development of efficient algorithms for representing and manipulating vine copula structures is also an active area of investigation.