Sensitivity analysis for probability assessments in Bayesian networks

When eliciting a probability model from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the experts intuition. This paper presents a methodology for analytic computation of sensitivity values in Bayesian network models. Sensitivity values are partial derivatives of output probabilities with respect to parameters being varied in the sensitivity analysis. They measure the impact of small changes in a network parameter on a target probability value or distribution. Sensitivity values can be used to focus knowledge elicitation effort on those parameters having the most impact on outputs of concern. Analytic sensitivity values are computed for an example and compared to sensitivity analysis by direct variation of parameters.>