Bayesian methods for simultaneous clustering and variable selection

用于同时聚类和变量选择的贝叶斯方法

• 批准号: 2021831
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2021831
• 关键词: Bayesian; methods; simultaneous; clustering; variable

The project aims to extend Bayesian clustering, mixture or factor analysis models to incorporate automatic feature selection. This is a challenging problem, to find latent structure in high-dimensional data whilst simultaneously discovering the variables which best predict this structure. The research is motivated by applications in chronic disease epidemiology, integrating multiple high-dimensional data sets simultaneously, enabling researchers to model mediation effects of intermediate exposures and biological markers. Areas of application may be in epidemiology, health economics or bioinformatics.

该项目旨在扩展贝叶斯聚类、混合或因子分析模型以纳入自动特征选择。这是一个具有挑战性的问题，要找到高维数据中的潜在结构，同时发现最能预测该结构的变量。该研究的动机是在慢性病流行病学中的应用，同时整合多个高维数据集，使研究人员能够模拟中间暴露和生物标记物的中介效应。应用领域可能是流行病学、卫生经济学或生物信息学。