Applying, developing and evaluating Bayesian Network structure learning algorithms to complex real-world datasets .

将贝叶斯网络结构学习算法应用、开发和评估到复杂的现实世界数据集。

• 批准号: 2441682
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2441682
• 关键词: Applying; developing; evaluating; Bayesian; Network

There has been continued advances of machine learning techniques to learn causal models (Bayesian Networks) from data in order to understand complex real world systems and predict the effect of interventions in them. However much of the progress has been on learning and evaluating synthetic models, with many real-world aspects such as missing or noisy data, dynamic evolution of the system and unmeasured variables being relatively neglected. Moreover, there has been less focus on integrating machine learning with expert knowledge and experimental interventions, as well as explaining why the machine learning algorithms produce the models that they do. This research will focus on addressing these issues in order to produce better and more explainable causal models of real-world systems in, for example, the health, social and environmental domains.

从数据中学习因果模型（贝叶斯网络）的机器学习技术一直在继续进步，以了解复杂的现实世界系统并预测干预措施的影响。然而，许多进步都是在学习和评估综合模型上，具有许多真实的方面，例如缺失或嘈杂的数据，系统的动态演变以及未衡量的变量相对忽略。此外，较少关注的是将机器学习与专家知识和实验干预措施集成，并解释了机器学习算法为什么生成它们所做的模型。这项研究将着重于解决这些问题，以便在例如健康，社会和环境领域中产生更好，更可解释的现实世界系统的因果模型。