Abstract In this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a very general approach to possibilistic identification problems in the framework of imprecise probabilities, i.e. the non-parametric estimation of possibility distributions of uncertain variables from data with a clear interpretation.

中文翻译：

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基于数据的可能性分布估计

摘要 在本文中，我们展示了不确定性的可能性描述如何在统计数据分析中非常自然地出现。结合最近的反向不确定性传播结果和边际可能性分布的一致聚合，这种估计程序能够在不精确概率的框架内提供一种非常通用的方法来解决可能性识别问题，即不确定变量的可能性分布的非参数估计来自有明确解释的数据。