Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g

The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the

When the amount of contamination is known, this example illustrates three different ways of performing

An example to show covariance estimation with the Mahalanobis distances on Gaussian distributed data. For Gaussian distributed

The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to