  We consider the effect of parametric uncertainty on properties of Linear Time
Invariant systems. Traditional approaches to this problem determine the
worst-case gains of the system over the uncertainty set. Whilst such approaches
are computationally tractable, the upper bound obtained is not necessarily
informative in terms of assessing the influence of the parameters on the system
performance. We present theoretical results that lead to simple, convex
algorithms producing parametric bounds on the $\mathcal{L}_2$-induced
input-to-output and state-to-output gains as a function of the uncertain
parameters. These bounds provide quantitative information about how the
uncertainty affects the system.
