The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Making Sensitivity Analysis Computationally Efficient
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Sensitive Analysis for Threshold Decision Making with Bayesian Belief Networks
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
How to elicit many probabilities
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems
Artificial Intelligence in Medicine
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With the advance of efficient algorithms for sensitivity analysis of probabilistic networks, studying the sensitivities revealed by real-life networks is becoming feasible. As the amount of data yielded by an analysis of even a moderatelysized network is already overwhelming, effective methods for extracting relevant information from these data are called for. One such method is to study the derivatives of the sensitivity functions yielded, to identify the parameters that upon variation are expected to have a large effect on a probability of interest. We further propose to build upon the concept of admissible deviation, which captures the extent to which a parameter can be varied without inducing a change in the most likely outcome. We illustrate these concepts by means of a sensitivity analysis of a reallife probabilistic network in the field of oncology.