2013/02/20 by Gregory M. Provan, Provan, Gregory M.
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.4979
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
arxiv created 2013/02/20 · arxiv updated 2013/02/21
Bayesian belief networks are bing increasingly used as a knowledge representation for diagnostic reasoning. One simple method for conducting diagnostic reasoning is to represent system faults and observations only. In this paper, we investigate how having intermediate nodes-nodes other than fault and observation nodes affects the diagnostic performance of a Bayesian belief network. We conducted a series of experiments on a set of real belief networks for medical diagnosis in liver and bile disease. We compared the effects on diagnostic performance of a two-level network consisting just of disease and finding nodes with that of a network which models intermediate pathophysiological disease states as well. We provide some theoretical evidence for differences observed between the abstracted two-level network and the full network.