Computes the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality.

For a sample matrix, x, we compute the sample covariance matrix of the data as the maximum likelihood estimator (MLE) of the population covariance matrix.

cov_mle(x, diag = FALSE)

Arguments

x	data matrix with n observations and p feature vectors
diag	logical value. If TRUE, assumes the population covariance matrix is diagonal. By default, we assume that diag is FALSE.

Value

sample covariance matrix of size \(p \times p\). If diag is TRUE, then a vector of length p is returned instead.

Details

If the diag option is set to TRUE, then we assume the population covariance matrix is diagonal, and the MLE is computed under this assumption. In this case, we return a vector of length p instead.