This paper considers updating information and statistical inference based on generalized credal sets. In the first part, we define possible ways of conditioning for generalized credal sets in a finite setting, and its realization based on generalized upper previsions with applications to decision making. We compare the obtained results with the generalized Bayes rule from the theory of imprecise probabilities, with Dempster's rule from the theory of belief functions, and with the models of imprecise probabilities based on the likelihood function proposed by M. Cattaneo.

中文翻译：

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基于广义凭证集更新信息。第 1 部分：有限情况

本文考虑基于广义凭证集的信息更新和统计推断。在第一部分中，我们定义了有限环境中广义信用集条件化的可能方法，及其基于广义上限预测的实现及其在决策中的应用。我们将所得结果与不精确概率理论中的广义贝叶斯规则、置信函数理论中的登普斯特规则以及M. Cattaneo 提出的基于似然函数的不精确概率模型进行了比较。