Minimax Filtering via Relations between Information and Estimation

TL;DR

This paper develops a minimax causal estimator for continuous-time signals, showing it is Bayesian and linking its worst-case regret to channel capacity, with explicit solutions for Gaussian and Poisson channels.

Contribution

It characterizes the minimax estimator as a Bayesian estimator and relates minimax regret to channel capacity, providing explicit solutions for Gaussian and Poisson models.

Findings

Minimax regret equals channel capacity for deterministic signals.

The minimax estimator is the Bayesian estimator with the capacity-achieving prior.

Examples demonstrate constructing minimax filters via capacity approximation.

Abstract

We investigate the problem of continuous-time causal estimation under a minimax criterion. Let $X^T = \{X_t,0\leq t\leq T\}$ be governed by the probability law $P_{\theta}$ from a class of possible laws indexed by $\theta \in \Lambda$, and $Y^T$ be the noise corrupted observations of $X^T$ available to the estimator. We characterize the estimator minimizing the worst case regret, where regret is the difference between the causal estimation loss of the estimator and that of the optimum estimator. One of the main contributions of this paper is characterizing the minimax estimator, showing that it is in fact a Bayesian estimator. We then relate minimax regret to the channel capacity when the channel is either Gaussian or Poisson. In this case, we characterize the minimax regret and the minimax estimator more explicitly. If we further assume that the uncertainty set consists of deterministic signals, the worst case regret is exactly equal to the corresponding channel capacity, namely the maximal mutual information attainable across the channel among all possible distributions on the uncertainty set of signals. The corresponding minimax estimator is the Bayesian estimator assuming the capacity-achieving prior. Using this relation, we also show that the capacity achieving prior coincides with the least favorable input. Moreover, we show that this minimax estimator is not only minimizing the worst case regret but also essentially minimizing regret for "most" of the other sources in the uncertainty set. We present a couple of examples for the construction of an minimax filter via an approximation of the associated capacity achieving distribution.