covariance matrices


A covariance matrix generalizes the idea of variance to multiple dimensions, where the i-th j-th element in the covariance matrix is the covariance between the i-th and j-th random variables. Covariance matrices are common throughout both statistics and machine learning and often arise when dealing with multivariate distributions.


This concept has the prerequisites:


  • Understand how to calculate the entries of a covariance matrix
  • Understand the difference between positive and negative covariances

Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

Supplemental resources (the following are optional, but you may find them useful)


See also