Uncertainty in Artificial Intelligence
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Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?
Max Henrion, Malcolm Pradhan, Brendan del Favero, Kurt Huang, Gregory Provan, Paul O'Rorke
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, the authors have recently completed an extensive study in which they applied random noise to the numerical probabilities in a set of belief networks for medical diagnosis, subsets of the CPCS network, a subset of the QMR (Quick Medical Reference) focused on liver and bile diseases. The diagnostic performance in terms of the average probabilities assigned to the actual diseases showed small sensitivity even to large amounts of noise. In this paper, we summarize the findings of this study and discuss possible explanations of this low sensitivity. One reason is that the criterion for performance is average probability of the true hypotheses, rather than average error in probability, which is insensitive to symmetric noise distributions. But, we show that even asymmetric, logodds-normal noise has modest effects. A second reason is that the gold-standard posterior probabilities are often near zero or one, and are little disturbed by noise.
Keywords: Bayesian belief networks, BN20, CPCS, diagnosis, noisy leaky OR, QMR, qualitative re
Pages: 307-314
PS Link: http://www.coginst.uwf.edu/~paulo/96UAISensitivity.ps
PDF Link: /papers/96/p307-henrion.pdf
AUTHOR = "Max Henrion and Malcolm Pradhan and Brendan del Favero and Kurt Huang and Gregory Provan and Paul O'Rorke",
TITLE = "Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "307--314"

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