论文标题
通过贝叶斯网络的概率培养皿网的不确定性推理
Uncertainty Reasoning for Probabilistic Petri Nets via Bayesian Networks
论文作者
论文摘要
本文利用了扩展的贝叶斯网络来对培养皿网的不确定性推理，在培养皿中，过渡是概率的。特别是，贝叶斯网络用作概率分布的符号表示，对观察者在网络中的标记的了解进行建模。观察者可以通过监视成功和失败的步骤来研究网络。 贝叶斯网的更新机制可以通过放松一些限制来实现，从而导致模块化的贝叶斯网，可以方便地表示和修改。至于每个符号表示，问题是如何从模块化的贝叶斯网中得出信息（在这种情况下是边缘概率分布）。我们通过概括已知的可变消除方法来展示如何做到这一点。 该方法通过有关疾病（SIR模型）和社交网络中信息扩散的示例来说明。我们已经实施了方法并提供运行时结果。
This paper exploits extended Bayesian networks for uncertainty reasoning on Petri nets, where firing of transitions is probabilistic. In particular, Bayesian networks are used as symbolic representations of probability distributions, modelling the observer's knowledge about the tokens in the net. The observer can study the net by monitoring successful and failed steps. An update mechanism for Bayesian nets is enabled by relaxing some of their restrictions, leading to modular Bayesian nets that can conveniently be represented and modified. As for every symbolic representation, the question is how to derive information - in this case marginal probability distributions - from a modular Bayesian net. We show how to do this by generalizing the known method of variable elimination. The approach is illustrated by examples about the spreading of diseases (SIR model) and information diffusion in social networks. We have implemented our approach and provide runtime results.
