A Bayesian approach to sensitivity analysis
提出基于信息价值的贝叶斯敏感性分析方法，相比传统距离或概率度量，能更好回应决策者真实关切，并用实例对比两种方法。
Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures. We advocate a new approach based on information value and argue that such an approach is better suited to address the decision-maker's real concerns. We provide an example comparing conventional sensitivity analysis to one based on information value. This article is a US government work and is in the public domain in the United States.