  Despite the popularity of information measures in analysis of probabilistic
systems, proper tools for their visualization are not common. This work
develops a simple matrix representation of information transfer in sequential
systems, termed information matrix (InfoMat). The simplicity of the InfoMat
provides a new visual perspective on existing decomposition formulas of mutual
information, and enables us to prove new relations between sequential
information theoretic measures. We study various estimation schemes of the
InfoMat, facilitating the visualization of information transfer in sequential
datasets. By drawing a connection between visual patterns in the InfoMat and
various dependence structures, we observe how information transfer evolves in
the dataset. We then leverage this tool to visualize the effect of
capacity-achieving coding schemes on the underlying exchange of information. We
believe the InfoMat is applicable to any time-series task for a better
understanding of the data at hand.
