可能性网络：不精确数据的参数学习及评价策略
Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy
摘要：
有越来越多的兴趣在多学科的研究表示和推理与不完美的数据。可能性网络提出了一个强大的框架，用于表示不确定和不精确的信息。本文讨论了从不精确数据集学习其参数的问题，即，包含多值数据。在本文的第一部分，我们提出了一个可能性网络抽样过程。在第二部分中，我们提出了一个似然函数，探讨随机集理论和可能性理论之间的联系。然后，该函数被部署为参数化可能性网络。
英文摘要：
There has been an ever-increasing interest in multidisciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the rst part of this paper a possibilistic networks sampling process. In the second part, we propose a likelihood function which explores the link between random sets theory and possibility theory. This function is then deployed to parametrize possibilistic networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
