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.

翻译：尽管信息测度在概率系统分析中应用广泛，但相应的可视化工具并不常见。本研究开发了一种用于表示序列系统中信息传递的简单矩阵形式，称为信息矩阵（InfoMat）。InfoMat的简洁性为现有互信息分解公式提供了新的可视化视角，并使我们能够证明序列信息论测度之间的新关系。我们研究了InfoMat的多种估计方案，促进了序列数据集中信息传递的可视化分析。通过建立InfoMat中的视觉模式与各类依赖结构之间的关联，我们观察到数据集中信息传递的演化过程。随后，我们利用该工具可视化容量逼近编码方案对底层信息交换的影响。我们相信InfoMat可适用于任何时间序列任务，以增进对当前数据的理解。