Bayesian methods for simultaneous clustering and variable selection

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，在高维数据中找到潜在的结构，同时发现最能预测这种结构的变量。该研究的动机是在慢性疾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志物的中介效应。应用领域可能是流行病学、卫生经济学或生物信息学。

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.