As noted, the inverse of I predicts the parameter covariance matrix assuming that 2  depends linearly on parameter values. However, if the dependence is not linear over a sufficiently large span of parameter values, or if other conditions hold, then the inverse of I gives only a lower bound on the marginalized variances and covariances, called the Cramér-Rao lower bound. The qualifier marginalized is importantit implies that the method gives variances that are larger than they would be if the parameters were varied independently because couplings and mutual compensatory behaviors among parameters are considered.