标题： 可能性推断模型：综述 显示英文标题

标题： Possibilistic inferential models: a review

摘要： 推断模型（IM）是一种描述可证明可靠的、数据驱动的关于相关未知数的不确定性量化和推断的模型。 IM 和费舍尔的置信推断有相似的目标，但两者之间的一个根本区别在于前者不需要不确定性量化是概率性的，这提供了更大的灵活性，并允许证明其可靠性。 由于与不精确概率文献，特别是可能性理论的新发现联系，最近取得了重要进展。 本文研究的可能性IM是一种易于构建的模型，具有非常强的类似频率论的可靠性特性，并提供完全条件的、类似贝叶斯（不精确）的概率推理。 本文回顾了这些关键的最新进展，描述了新理论、方法和计算工具。 还提出了基本可能性IM的一种推广，使其与现代统计学和机器学习中的概念建立了新的和意想不到的联系，例如，自举法和同构预测。 显示英文摘要

摘要： 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.