This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution for a rare event $A$. More specifically, assuming there exists a decision-making process closely related to $A$ which forms a decision $Y$, where a history of decisions have been collected. By modelling the data observed to make the historic decisions, using a Bayesian model, an analyst can infer a distribution for the parameters of the random variable $Y$. This distribution can be used to approximate the prior distribution for the parameters of the random variable for event $A$. This method is novel in the field of prior elicitation and has the potential of improving upon current methods by using real-life decision-making processes, that can carry real-life consequences, and, because it does not require an expert to have statistical knowledge. Future decision making can be improved upon using this method, as it highlights variables that are impacting the decision making process. An application for eliciting a prior distribution of recidivism, for an individual, is used to explain this method further.

翻译：本文提出了一种从专家处引导先验分布的新方法。该方法对专家决策过程进行建模，以推断罕见事件$A$的先验概率分布。具体而言，假设存在一个与$A$密切相关的决策过程，该过程形成决策$Y$，并已收集到决策历史。通过利用贝叶斯模型对做出历史决策时所观察到的数据进行建模，分析人员可以推断出随机变量$Y$参数的分布。该分布可用于近似逼近事件$A$的随机变量参数的先验分布。此方法在先验引导领域具有创新性，通过利用具有实际后果的真实决策过程，且无需专家具备统计知识，从而有潜力改进现有方法。该方法还能突出影响决策过程的变量，进而改进未来决策。本文通过一个针对个体再犯先验分布的引导应用实例，进一步阐释了该方法。