Visualizing the Consequences of Evidence in Bayesian 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.
