  We consider an input $x$ generated by an unknown stationary ergodic source
$X$ that enters a signal processing system $J$, resulting in $w=J(x)$. We
observe $w$ through a noisy channel, $y=z(w)$; our goal is to estimate x from
$y$, $J$, and knowledge of $f_{Y|W}$. This is universal estimation, because
$f_X$ is unknown. We provide a formulation that describes a trade-off between
information complexity and noise. Initial theoretical, algorithmic, and
experimental evidence is presented in support of our approach.
