Bayesian variable selection in high-dimensional latent variable models

高维潜变量模型中的贝叶斯变量选择

• 批准号: 2467837
• 金额: --
• 依托单位: University of Kent
• 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-2467837
• 关键词: Bayesian; variable; selection; dimensional; latent

The project will develop new methodological and computational tools for performing efficient Bayesian variable selection in hierarchical models that include large numbers of latent variables. The successful candidate will build on recent work by the supervisory team [1][2] and will extend the work in a number of directions, developing sophisticated and efficient algorithms for fast computation in complex models, while developing associated tools, such as R packages, to ensure dissemination of the methods. An example application of the work is the recent increase in availability of satellite remote-sensing imagery to measure the environment in detail [3], which gives rise to hundreds or thousands of covariates that, in combination with sophisticated statistical models for ecological data, can revolutionise landscape and conservation planning. [1] https://discovery.ucl.ac.uk/id/eprint/10098647/ [2] https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12390?af=R [3] https://www.nature.com/articles/s41559-017-0176

该项目将开发新的方法论和计算工具，用于在包括大量潜在变量的层次模型中执行有效的贝叶斯变量选择。成功的候选人将基于监督团队[1] [2]的最新工作，并将在许多方向上扩展工作，开发复杂模型中快速计算的复杂且有效的算法，同时开发相关工具，例如R套件，确保方法传播。这项工作的一个示例应用是卫星遥感图像的最新可用性提高以详细测量环境[3]，这会产生数百或数千个协变量，这些协变量与生态数据的复杂统计模型结合在一起，可以可以彻底改变了景观和保护计划。 [1] https://discovery.ucl.ac.uk/id/eprint/10098647/ [2] https://rss.onlinelibrary.wiley.wiley.com/doi/boi/doi/abs/10.1111/rssc.12390？ 3] https://www.nature.com/articles/s41559-017-0176