Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While popular methods rely on asking experts probability based questions to quantify uncertainty, these methods are not without their drawbacks and many alternative elicitation methods exist. This paper explores methods for eliciting informative priors categorized by type and briefly discusses their strengths and limitations. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods for eliciting informative priors for these problems. The primary aim of this work is to highlight some of the gaps in the present state of art and identify directions for future research.

翻译：在Bayesian推论中,人们通常会以询问专家概率问题来量化不确定性为常用方法,但这种方法并非没有缺点,而且存在许多不同的推论方法;本文件探讨了按类型分类收集信息前科的方法,并简要讨论了其优点和局限性;自始至终,使用两个具有代表性的应用方法来探讨为这些问题收集信息前科的现有方法是否合适,或缺乏这些方法;这项工作的主要目的是突出目前最新技术中的一些差距,并确定今后研究的方向。