  Knowledge elicitation is one of the major bottlenecks in expert system
design. Systems based on Bayes nets require two types of information--network
structure and parameters (or probabilities). Both must be elicited from the
domain expert. In general, parameters have greater opacity than structure, and
more time is spent in their refinement than in any other phase of elicitation.
Thus, it is important to determine the point of diminishing returns, beyond
which further refinements will promise little (if any) improvement. Sensitivity
analyses address precisely this issue--the sensitivity of a model to the
precision of its parameters. 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 structure and parameters to system performance,
as well as the sensitivity of a system based on a Bayes net to noise in its
assessed parameters.
