Multivariate moments

Multivariate moments are statistical measures that describe the properties of multiple variables taken together. They extend the idea of moments from single variables (like average and variance) to multiple dimensions, capturing not just individual behaviors but also how variables relate to each other. For example, the mean vector indicates the central tendency, while the covariance matrix shows how variables vary together. Higher-order moments provide insights into the shape and dependency structures of complex data, helping us understand patterns and relationships in multivariate data sets more comprehensively.