A Sensitivity Analysis of Pathfinder

Knowledge elicitation is one of the major bot­ tlenecks in expert system design. Systems based on Bayes nets require two types of information-network structure and parame­ ters (or probabilities) . Both must be elicited from the domain expert. In general, parame­ ters have greater opacity than structure, and more time is spent in their refinement than in any other phase of elicitation. Thus, it is im­ portant to determine the point of diminishing returns, beyond which further refinements will promise little (if any) improvement. Sensitivity analyses address precisely this issue-the sensi­ tivity of a model to the precision of its param­ eters. In this paper, we report the results of a sensitivity analysis of Pathfinder, a Bayes net­ based system for diagnosing pathologies of the lymph system. This analysis is intended to shed some light on the relative importance of struc­ ture and parameters to system performance, as well as the sensitivity of a system based on a Bayes net to noise in its assessed parameters.