Optimal Learning from the Doob-Dynkin lemma

Abstract

The Doob-Dynkin Lemma gives conditions on two functions $X$ and $Y$ that ensure existence of a function ${\phi}$ so that $X = {\phi} \circ Y$. This communication proves different versions of the Doob-Dynkin Lemma, and shows how it is related to optimal statistical learning algorithms. Keywords and phrases: Improper prior, Descriptive set theory, Conditional Monte Carlo, Fiducial, Machine learning, Complex data.