Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Analysing sensitivity data from probabilistic networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Same-decision probability: A confidence measure for threshold-based decisions
International Journal of Approximate Reasoning
An exact algorithm for computing the same-decision probability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The probability assessments of a Bayesian belief network generally include inaccuracies. These inaccuracies influence the reliability of the network's output. An integral part of investigating the output's reliability is to study its robustness. Robustness pertains to the extent to which varying the probability assessments of the network influences the output. It is studied by subjecting the network to a sensitivity analysis. In this paper, we address the issue of robustness of a belief network's output in view of the threshold model for decision making. We present a method for sensitivity analysis that provides for the computation of bounds between which a network's assessments can be varied without inducing a change in recommended decision.