基于ROBDD的贝叶斯网络推理及故障排除
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
摘要：
当使用贝叶斯网络对人造机械的行为进行建模时，通常会发生模型的大部分是确定性的。对于这种贝叶斯网络，模型的确定性部分可以表示为布尔函数，并且置信更新的中心部分简化为计算布尔函数中满意配置的数量的任务。在本文中，我们将探讨如何在计算布尔函数的进步，可以通过信念更新，特别是在故障排除的背景下。我们目前的实验结果表明，一个显着的速度相比，传统的连接树传播。
英文摘要：
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In this paper we explore how advances in the calculation of Boolean functions can be adopted for belief updating, in particular within the context of troubleshooting. We present experimental results indicating a substantial speed-up compared to traditional junction tree propagation.
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。
