Information Complexity and Estimation

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.

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