def estimate_data(samples, cov, t):
# XX is a full data matrix
# Cov is the covariance matrix of an underlying Gaussian graphical model
T = binarize_edges(np.linalg.inv(cov), t)
E = binarize_edges(estimate_precision(samples), t)
fpr, fnr, fnorm = compute_statistics(T, E)
return fpr, fnr, fnorm
