Probabilistic Graphical Models For Interventional Queries

介入查询的概率图形模型

• 批准号: DP160100703
• 负责人: Prof Javen Qinfeng Shi
• 金额: $ 22.29万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160100703
• 关键词: Probabilistic; Graphical; Models; Interventional; Queries

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.

该项目打算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符合我们的利益。概率图模型能够对大量变量之间的复杂关系和交互进行建模，从而促进了许多应用的发展。然而，此类模型仅旨在了解底层环境。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，从而改变环境以最适合我们的利益。拟议的项目旨在利用大量现实世界数据集的概率图形模型来实现这一目标，从而促进从医疗保健到商业和环境的各种应用。