Bayesian updating of atomic probabilities
研究了在无结果偏好情境下，从原子概率测度出发，通过最小要求关系假设或其他更强假设，刻画了唯一允许贝叶斯更新的概率测度族，该族中拉普拉斯公式可用于确定事件概率。
The standard conditional probability formula is supposed to reflect the correct updating of probability assignments when new information is incorporated (Bayesian updating). We consider a context with no preferences on outcomes. Starting from an atomic probability measure and assuming a “minimum requirement” relational assumption or other stronger assumptions, we characterize the family of probability measures where Bayesian updating is the only possibility. This family turns out to be formed by those probability measures in which the Laplace formula can be used to determine the probability of events.