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Recent research has found that diagnostic performance with Bayesian beliefnetworks is often surprisingly insensitive to imprecision in the numericalprobabilities. For example, the authors have recently completed an extensivestudy in which they applied random noise to the numerical probabilities in aset of belief networks for medical diagnosis, subsets of the CPCS network, asubset of the QMR (Quick Medical Reference) focused on liver and bile diseases.The diagnostic performance in terms of the average probabilities assigned tothe actual diseases showed small sensitivity even to large amounts of noise. Inthis paper, we summarize the findings of this study and discuss possibleexplanations of this low sensitivity. One reason is that the criterion forperformance is average probability of the true hypotheses, rather than averageerror in probability, which is insensitive to symmetric noise distributions.But, we show that even asymmetric, logodds-normal noise has modest effects. Asecond reason is that the gold-standard posterior probabilities are often nearzero or one, and are little disturbed by noise.

Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?