Methods for probability updating, of which Bayesian conditionalization is the most well-known and widely used, are modeling tools that aim to represent the process of modifying an initial epistemic state, typically represented by a prior probability function P, which is adjusted in light of new information. Notably, updating methods and conditional sentences seem to intuitively share a deep connection, as is evident in the case of conditionalization. The present work contributes to this line of research and aims at shedding new light on the relationship between updating methods and conditional connectives. Departing from previous literature that often focused on a specific type of conditional or a particular updating method, our goal is to prove general results concerning the connection between conditionals and their probabilities. This will allow us to characterize the probabilities of certain conditional connectives and to understand what class of updating procedures can be represented using specific conditional connectives. Broadly, we adopt a general perspective that encompasses a large class of conditionals and a wide range of updating methods, enabling us to prove some general results concerning their interrelation.

翻译：概率更新方法（其中贝叶斯条件化最为著名且广泛应用）是一类建模工具，旨在表征对初始认知状态（通常由先验概率函数P表示）进行修正的过程，该过程根据新信息进行调整。值得注意的是，更新方法与条件句在直觉上似乎存在深刻关联，这在条件化案例中尤为明显。本研究延续这一研究方向，旨在重新审视更新方法与条件连接词之间的关系。不同于以往文献常聚焦于特定类型的条件句或特定更新方法，我们的目标是证明关于条件句与其概率之间联系的普遍性结论。这将使我们能够刻画特定条件连接词的概率特征，并理解哪些类别的更新过程可通过特定条件连接词进行表征。总体而言，我们采用一种涵盖广泛条件句类别与多样化更新方法的通用视角，从而能够证明关于二者相互关系的若干普遍性结论。