Variations on a Theme by Liu, Cuff, and Verdú: The Power of Posterior Sampling

Abstract

The Liu-Cuff-Verdu lemma states that in estimating a source X from an observation Y, making a random guess X' from the posterior p(xly) can go wrong at most twice as often as the optimal answer. Several variations of this fundamental, yet rather arcane, result are explored for detection, decoding, and estimation problems.