Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Printer troubleshooting using Bayesian networks
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Network Engineering for Agile Belief Network Models
IEEE Transactions on Knowledge and Data Engineering
DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Accuracy vs. efficiency trade-offs in probabilistic diagnosis
Eighteenth national conference on Artificial intelligence
When plans distinguish Bayes nets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Knowledge acquisition for diagnosis model in wireless networks
Expert Systems with Applications: An International Journal
Bayesian prediction of an epidemic curve
Journal of Biomedical Informatics
PCOPM: a probabilistic CBR framework for obesity prescription management
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Making sensitivity analysis computationally efficient
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Analysing sensitivity data from probabilistic networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
A multi-agent intelligent environment for medical knowledge
Artificial Intelligence in Medicine
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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, Iogodds-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.