Bayes-like Decision Making with Upper and Lower Probabilities
研究了用区间值概率表示参数假设的支持度，在已知似然函数和先验信息但对其信心不足时，探讨可接受、连贯的决策规则，并试图保留贝叶斯决策的要素而不做出无根据的知识主张。
Abstract We consider the use of interval-valued probabilities to represent the support lent to the hypothesis that the parameter value θ lies in a subset A of the parameter set Θ when we observe x, know the likelihoods {fΘ: θεΘ}, and have some prior information concerning the parameter. Our model for prior information is that of a salient prior distribution in which we have little confidence, although we have much less confidence in any alternative prior. We consider notions of acceptable and coherent decision making as well as notions of being able to achieve a Bayes rule and least commitment. Throughout we are motivated to preserve some of the elements of Bayesian decision making without thereby committing ourselves to unwarranted claims of knowledge.