Bayesian variable selection in high-dimensional latent variable models

该项目将开发新的方法和计算工具，用于在包括大量潜在变量的分层模型中进行有效的贝叶斯变量选择。成功的候选人将建立在监督团队最近的工作基础上[1][2]，并将在多个方向上扩展工作，开发复杂模型中快速计算的复杂高效算法，同时开发相关工具，如R包，以确保方法的传播。这项工作的一个应用实例是最近增加了卫星遥感图像的可用性，以详细测量环境[3]，这产生了数百或数千个协变量，与复杂的生态数据统计模型相结合，可以彻底改变景观和保护规划。[1]https://discovery.ucl.ac.uk/id/eprint/10098647/https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12390? [2] [3]第三届https://www.nature.com/articles/s41559-017-0176

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