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 propose a generalization of the mutual information definition, arguing our choice on an interpretation based on an analogy with Global Sensitivity Analysis. 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，原因在于先验选择可能具有主观性。当所研究的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，该理论处理了一种可称为客观的非信息先验的构建。我们提出了一种互信息定义的推广，并基于全局敏感性分析的类比解释来论证我们的选择。研究了我们广义度量的一类性质，结果强化了Jeffreys先验选择，该选择满足我们对参考先验的扩展定义。