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.

翻译：定性概率网络（QPNs）结合了贝叶斯网络的条件独立性假设与正负依赖的定性属性。它形式化了正依赖的各种直观性质，从而支持对大规模变量网络进行推理。然而，本文将证明，由于一项不正确的对称性性质，非二元QPNs中获得的许多推理在数学上并不成立。我们将给出此类错误推理的示例，并简要讨论可能的解决方案。