Bounded Turing Reductions and Data Processing Inequalities for Sequences

Abstract

A data processing inequality states that the quantity of shared information between two entities (e.g. signals, strings) cannot be significantly increased when one of the entities is processed by certain kinds of transformations. In this paper, we prove several data processing inequalities for sequences, where the transformations are bounded Turing functionals and the shared information is measured by the lower and upper mutual dimensions between sequences.We show that, for all sequences X, Y, and Z, if Z is computable Lipschitz reducible to X, thenWe also show how to derive different data processing inequalities by making adjustments to the computable bounds of the use of a Turing functional. The yield of a Turing functional Φ S with access to at most n bits of the oracle S is the smallest input m ∈ N such that Φ S↾n (m) ↑. We show how to derive reverse data processing inequalities (i.e., data processing inequalities where the transformation may significantly increase the shared information between two entities) for sequences by applying computable bounds to the yield of a Turing functional.

Mutual dimension, data processing inequalities, and randomness

“…We define the yield of a Turing functional Φ S with access to at most n symbols of S to be the smallest m ∈ N such that Φ S n (m) does not halt and show how to derive reverse data processing inequalities by applying bounds to the Turing functional's yield. The rest of this chapter can be found in [8].…”