In Bayesian analysis, reference priors are widely recognized for their objective nature. Yet, they often lead to intractable and improper priors, which complicates their application. Besides, informed prior elicitation methods are penalized by the subjectivity of the choices they require. In this paper, we aim at proposing a reconciliation of the aforementioned aspects. Leveraging the objective aspect of reference prior theory, we introduce two strategies of constraint incorporation to build tractable reference priors. One provides a simple and easy-to-compute solution when the improper aspect is not questioned, and the other introduces constraints to ensure the reference prior is proper, or it provides proper posterior. Our methodology emphasizes the central role of Jeffreys prior decay rates in this process, and the practical applicability of our results is demonstrated using an example taken from the literature.

翻译：在贝叶斯分析中，参考先验因其客观性而得到广泛认可。然而，这类先验常导致难以处理的非正常先验，从而增加了实际应用的复杂性。此外，基于信息的先验设定方法常因其所需选择的主观性而受到制约。本文旨在调和上述矛盾，通过借鉴参考先验理论的客观性本质，提出两种约束整合策略以构建可处理的参考先验。第一种策略在无需质疑非正常性的情况下提供简洁易计算的解决方案；第二种策略则通过引入约束确保参考先验具有正常性，或至少能产生正常后验。我们的方法论特别强调Jeffreys先验衰减率在此过程中的核心作用，并通过文献案例展示了所提结果的实际适用性。