covariancelike
Covariancelike refers to a family of statistical measures that generalize the concept of covariance, extending its applicability beyond traditional pairwise relationships in data. While covariance quantifies the linear relationship between two random variables, covariancelike measures can assess more complex dependencies, including nonlinear associations, higher-order interactions, or relationships involving more than two variables.
In probability theory and statistics, covariance is defined for two variables as the expected value of their
These measures are particularly useful in fields such as machine learning, where understanding complex data structures
Despite their flexibility, covariancelike measures often introduce computational challenges, as they may require estimating high-dimensional matrices