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Practical challenges and methodological flexibility in prior elicitation.
Psychological Methods ( IF 10.929 ) Pub Date : 2020-09-17 , DOI: 10.1037/met0000354
Angelika M Stefan 1 , Nathan J Evans 1 , Eric-Jan Wagenmakers 1
Affiliation  

The Bayesian statistical framework requires the specification of prior distributions, which reflect predata knowledge about the relative plausibility of different parameter values. As prior distributions influence the results of Bayesian analyses, it is important to specify them with care. Prior elicitation has frequently been proposed as a principled method for deriving prior distributions based on expert knowledge. Although prior elicitation provides a theoretically satisfactory method of specifying prior distributions, there are several implicit decisions that researchers need to make at different stages of the elicitation process, each of them constituting important researcher degrees of freedom. Here, we discuss some of these decisions and group them into 3 categories: decisions about (a) the setup of the prior elicitation; (b) the core elicitation process; and (c) combination of elicited prior distributions from different experts. Importantly, different decision paths could result in greatly varying priors elicited from the same experts. Hence, researchers who wish to perform prior elicitation are advised to carefully consider each of the practical decisions before, during, and after the elicitation process. By explicitly outlining the consequences of these practical decisions, we hope to raise awareness for methodological flexibility in prior elicitation and provide researchers with a more structured approach to navigate the decision paths in prior elicitation. Making the decisions explicit also provides the foundation for further research that can identify evidence-based best practices that may eventually reduce the methodologically flexibility in prior elicitation. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译:

先前启发中的实际挑战和方法上的灵活性。

贝叶斯统计框架需要先验分布的规范,这反映了关于不同参数值的相对合理性的预数据知识。由于先验分布会影响贝叶斯分析的结果,因此必须小心指定它们。先验启发经常被提议作为一种基于专家知识推导先验分布的原则性方法。尽管先验启发提供了一种理论上令人满意的指定先验分布的方法,但研究人员需要在启发过程的不同阶段做出几个隐含的决定,每个决定都构成了重要的研究人员自由度。在这里,我们讨论了其中一些决定并将它们分为 3 类:关于 (a) 设置先验启发的决定;(b) 核心启发过程;(c) 来自不同专家的先验分布的组合。重要的是,不同的决策路径可能会导致从同一专家那里得出的先验差异很大。因此,建议希望进行事先启发的研究人员在启发过程之前、期间和之后仔细考虑每个实际决策。通过明确概述这些实际决策的后果,我们希望提高对先前启发中方法灵活性的认识,并为研究人员提供一种更有条理的方法来导航先前启发中的决策路径。明确决策也为进一步研究奠定了基础,这些研究可以确定基于证据的最佳实践,最终可能会降低先前启发中的方法灵活性。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)
更新日期:2020-09-17
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