论文标题
将精算判断与概率学习技术与图理论联系起来
Connecting actuarial judgment to probabilistic learning techniques with graph theory
论文作者
论文摘要
图形模型已被广泛用于从医学专家系统到自然语言处理的应用。它们的受欢迎程度部分出现了，因为它们是具有有效算法的变量中复杂依赖性的直观表示，用于在高维模型中执行计算强度的推断。有人认为，形式主义对于在非寿命保险索赔数据的建模中的应用非常有用。还表明，当前实践中的精算模型可以以图形方式表示以利用该方法的优势。在框架内提出了更多一般的模型，以证明使用远程信息处理和其他动态精算数据的图形模型用于概率学习的潜在使用。讨论还表明，整个模型的直观性质允许将定性知识或精算判断包括在分析中。
Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in high-dimensional models. It is argued that the formalism is very useful for applications in the modelling of non-life insurance claims data. It is also shown that actuarial models in current practice can be expressed graphically to exploit the advantages of the approach. More general models are proposed within the framework to demonstrate the potential use of graphical models for probabilistic learning with telematics and other dynamic actuarial data. The discussion also demonstrates throughout that the intuitive nature of the models allows the inclusion of qualitative knowledge or actuarial judgment in analyses.
