  Associated to each graph G is a Gaussian graphical model. Such models are
often used in high-dimensional settings, i.e. where there are relatively few
data points compared to the number of variables. The maximum likelihood
threshold of a graph is the minimum number of data points required to fit the
corresponding graphical model using maximum likelihood estimation. Graphical
lasso is a method for selecting and fitting a graphical model. In this project,
we ask: when graphical lasso is used to select and fit a graphical model on n
data points, how likely is it that n is greater than or equal to the maximum
likelihood threshold of the corresponding graph? Our results are a series of
computational experiments.
