In Bayesian theory, the role of information is central. The influence exerted by prior information on posterior outcomes often jeopardizes Bayesian studies, due to the potentially subjective nature of the prior choice. When the studied model is not enriched with sufficiently a priori information, the reference prior theory emerges as a proficient tool. Based on the mutual information criterion, the theory handles the construction of a non informative prior whose choice could be called objective. We unveil an original analogy between reference prior theory and Global Sensitivity Analysis, from which we propose a natural generalization of the mutual information definition. A class of our generalized metrics is studied and our results reinforce the Jeffreys' prior choice which satisfies our extended definition of reference prior.

翻译：在贝叶斯理论中，信息的作用至关重要。先验信息对后验结果的影响常常危及贝叶斯研究的可靠性，这源于先验选择潜在的主观性。当所研究模型缺乏充分的先验信息时，参考先验理论便成为一种有效的工具。基于互信息准则，该理论致力于构建一种可称为客观的非信息性先验。我们揭示了参考先验理论与全局敏感性分析之间的一种原始类比，并由此提出了互信息定义的自然推广。本文研究了一类广义度量，其结果强化了杰弗里斯先验选择，该选择满足我们对参考先验的扩展定义。