An empirical comparison of Bayesian and credal networks for dependable high-level information fusion

Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision making for this type of problems. A credal network is a graphical probabilistic method that utilizes sets of probability distributions, e.g., interval probabilities, for representation of belief. Such a representation allows one to properly express epistemic uncertainty, i.e., uncertainty that can be reduced if more information becomes available. Since reducing uncertainty has been proposed as one of the main goals of information fusion, the ability to represent epistemic uncertainty becomes an important aspect in all fusion applications.