Mutual Dimension and Random Sequences

Abstract: If $S$ and $T$ are infinite sequences over a finite alphabet, then the lower and upper mutual dimensions $mdim(S:T)$ and $Mdim(S:T)$ are the upper and lower densities of the algorithmic information that is shared by $S$ and $T$. In this paper we investigate the relationships between mutual dimension and coupled randomness, which is the algorithmic randomness of two sequences $R_1$ and $R_2$ with respect to probability measures that may be dependent on one another. For a restricted but interesting class of coupled probability measures we prove an explicit formula for the mutual dimensions $mdim(R_1:R_2)$ and $Mdim(R_1:R_2)$, and we show that the condition $Mdim(R_1:R_2) = 0$ is necessary but not sufficient for $R_1$ and $R_2$ to be independently random. We also identify conditions under which Billingsley generalizations of the mutual dimensions $mdim(S:T)$ and $Mdim(S:T)$ can be meaningfully defined; we show that under these conditions these generalized mutual dimensions have the "correct" relationships with the Billingsley generalizations of $dim(S)$, $Dim(S)$, $dim(T)$, and $Dim(T)$ that were developed and applied by Lutz and Mayordomo; and we prove a divergence formula for the values of these generalized mutual dimensions.