Gaussian Copula

Gaussian copulas are statistical tools used to model the dependence structure between multiple variables, regardless of their individual distributions. Current research focuses on extending their application beyond traditional Gaussian assumptions, employing techniques like optimal transport and semiparametric modeling to handle diverse data types (e.g., mixed continuous and discrete variables) and address challenges like high dimensionality and missing data. This flexibility allows Gaussian copulas to be effectively applied in various fields, including power system forecasting, clinical decision support, and high-performance computing, improving model accuracy and enabling more robust predictions and analyses. The development of efficient algorithms, such as adaptations of the PC algorithm, further enhances their practical utility.