Possibilistic Inferential Models: A Review
综述了可能性推理模型的最新进展，该模型提供可靠的不确定性量化，无需概率假设，具有频率学派可靠性，并支持贝叶斯式推理，还介绍了与自助法和共形预测等现代方法的联系。
An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher’s fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn’t require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfound connections with the imprecise probability literature, in particular, possibility theory. The brand of possibilistic IMs studied here are straightforward to construct, have very strong frequentist-like reliability properties, and offer fully conditional, Bayesian-like (imprecise) probabilistic reasoning. This paper reviews these key recent developments, describing the new theory, methods, and computational tools. A generalization of the basic possibilistic IM is also presented, making new and unexpected connections with ideas in modern statistics and machine learning, e.g., bootstrap and conformal prediction.