Visualizing the Consequences of Evidence in Bayesian Networks
This work addresses visualization problems for users of Bayesian networks, but it is incremental as it builds upon existing techniques.
The paper tackles the challenge of visualizing and navigating large, complex Bayesian networks by improving graphic design, enabling visual comparison of posterior distributions from different evidence sets, and using information theory to reduce visual complexity. Results from applying the methods to real-world data sets suggest they are useful for discovering information flow and comparing evidence configurations in large networks.
This paper addresses the challenge of viewing and navigating Bayesian networks as their structural size and complexity grow. Starting with a review of the state of the art of visualizing Bayesian networks, an area which has largely been passed over, we improve upon existing visualizations in three ways. First, we apply a disciplined approach to the graphic design of the basic elements of the Bayesian network. Second, we propose a technique for direct, visual comparison of posterior distributions resulting from alternative evidence sets. Third, we leverage a central mathematical tool in information theory, to assist the user in finding variables of interest in the network, and to reduce visual complexity where unimportant. We present our methods applied to two modestly large Bayesian networks constructed from real-world data sets. Results suggest the new techniques can be a useful tool for discovering information flow phenomena, and also for qualitative comparisons of different evidence configurations, especially in large probabilistic networks.