论文标题
贝叶斯对化学反应网络的验证
Bayesian Verification of Chemical Reaction Networks
论文作者
论文摘要
我们提出了一种数据驱动的验证方法，该方法确定给定的化学反应网络（CRN）是否满足给定特性，以模态逻辑中表示为公式。我们的方法包括三个阶段，将模型对模型的正式验证与从数据中学习进行了整合。首先，我们考虑基于已知的化学计量学的一组可能的模型集，并根据感兴趣的属性对其进行分类。其次，我们利用贝叶斯推断在参数模型中更新参数的概率分布，并从基础CRN收集数据。在第三阶段也是最后一个阶段，我们结合了两个步骤的结果，以计算基础CRN满足给定特性的概率。我们将新方法应用于案例研究，并将其与贝叶斯统计模型检查进行比较。
We present a data-driven verification approach that determines whether or not a given chemical reaction network (CRN) satisfies a given property, expressed as a formula in a modal logic. Our approach consists of three phases, integrating formal verification over models with learning from data. First, we consider a parametric set of possible models based on a known stoichiometry and classify them against the property of interest. Secondly, we utilise Bayesian inference to update a probability distribution of the parameters within a parametric model with data gathered from the underlying CRN. In the third and final stage, we combine the results of both steps to compute the probability that the underlying CRN satisfies the given property. We apply the new approach to a case study and compare it to Bayesian statistical model checking.
