The inferential model (IM) framework produces data-dependent, non-additive degrees of belief about the unknown parameter that are provably valid. The validity property guarantees, among other things, that inference procedures derived from the IM control frequentist error rates at the nominal level. A technical complication is that IMs are built on a relatively unfamiliar theory of random sets. Here we develop an alternative---and practically equivalent---formulation, based on a theory of possibility measures, which is simpler in many respects. This new perspective also sheds light on the relationship between IMs and Fisher's fiducial inference, as well as on the construction of optimal IMs.

翻译：推论模型(IM)框架产生对未知参数的依赖数据、不增加信任度的信念,这种信念是可行的,是可行的。有效的财产保障,除其他外,根据IM的推断程序在名义上控制常客误差率。技术复杂之处是,IMS建立在相对不熟悉的随机机组理论之上。在这里,我们开发了一种替代的、实际上等效的公式,其基础是可能性措施理论,在许多方面比较简单。这一新的观点还揭示了IMS与Fisher的推断之间的关系,以及最佳IMS的构建。