Qualitative probabilistic networks (QPNs) combine the conditional independence assumptions of Bayesian networks with the qualitative properties of positive and negative dependence. They formalise various intuitive properties of positive dependence to allow inferences over a large network of variables. However, we will demonstrate in this paper that, due to an incorrect symmetry property, many inferences obtained in non-binary QPNs are not mathematically true. We will provide examples of such incorrect inferences and briefly discuss possible resolutions.

中文翻译：

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关于非二元定性概率网络中错误推理的说明


定性概率网络 (QPN) 将贝叶斯网络的条件独立性假设与正相关性和负相关性的定性特性相结合。他们将正相关的各种直观属性形式化，以允许对大型变量网络进行推断。然而，我们将在本文中证明，由于不正确的对称性，在非二元 QPN 中获得的许多推论在数学上并不正确。我们将提供此类错误推论的示例，并简要讨论可能的解决方案。