We introduce a new method for updating subjective beliefs based on Jeffrey's rule of conditioning, called dynamic (precise) probability kinematics (DPK). We also give its generalization in order to work with sets of probabilities, called dynamic imprecise probability kinematics (DIPK). Updating a set of probabilities may be computationally costly. To this end, we provide bounds for the lower probability associated with the updated probability set, characterizing the set completely. The behavior of the updated sets of probabilities are studied, including contraction, dilation, and sure loss. We discuss the application of DPK and DIPK to survey sampling studies in which coarse and imprecise observations are anticipated.

翻译：我们根据Jeffrey的调节规则引入了一种更新主观信念的新方法,称为动态(精确)概率运动学(DPK),我们对此也进行了概括化,以便处理几组概率,称为动态不精确概率运动学(DIPK)。更新一套概率可能是计算成本高昂的。为此,我们提供了与更新概率集相关的较低概率的界限,将数据集完全定性为特征。正在研究更新的几组概率的行为,包括收缩、比值和肯定损失。我们讨论了DPK和DIPK在调查抽样研究中的应用情况,预计这些抽样研究中会出现粗劣和不准确的观测。