Using a New Tool to Visualize Environmental Data for Bayesian Network Modelling

This paper presents the software Omnigram Explorer, a visualization tool developed for interactive exploration of relations between variables in a complex system. Its objective is to help users gain an initial knowledge of their data and the relationships between variables. As an example, we apply it to the water reservoir data for Andalusia, Spain. Two Bayesian networks are learned using causal discovery, both with and without the information gleaned from this exploration process, and compared in terms of the Logarithmic loss and causal structure. Even though they show the same predictive accuracy, the initial exploration with Omnigram Explorer supported the use of prior information to achieve a more informative causal structure.