Probabilistic Graphical Models For Interventional Queries

该项目旨在开发方法来建议如何进行最佳干预，以便系统的未来状态最适合我们的利益。概率图形模型对大量变量之间的复杂关系和交互进行建模的能力为许多应用程序提供了便利。然而，这样的模型只是为了理解底层环境。在许多现实世界的应用中，最终需要的是建议我们应该如何干预或采取行动，以便改变环境以最适合我们的利益。拟议的项目旨在利用大量真实数据集的概率图形模型实现这一目标，从而促进从医疗保健到商业和环境的各种应用。

The project intends to develop methods to suggest how to optimally intervene so that the future state of the system will best suit our interests. The power of probabilistic graphical models to model complex relationships and interactions among a large number of variables facilitates many applications. However, such models only aim to understand the underlying environment. What is ultimately needed in many real-world applications is to suggest how we ought to intervene or act, so as to alter the environment to best suit our interests. The proposed project aims to achieve this using probabilistic graphical models on massive real-world data sets, thus facilitating a variety of applications from health care to commerce and the environment.