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.

dx.doi.org/10.1007/3-540-46238-4_4, hdl.handle.net/1765/133756
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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van der Gaag, L.C, & Coupé, V.M.H. (2000). Sensitivity analysis for threshold decision making with Bayesian belief networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/3-540-46238-4_4