Spatiotemporal Modelling

仔细模拟在生态学、流行病学和渔业科学等不同领域中至关重要的时空数据。随着数据集的大小增加，相关统计模型的复杂性也随之增加。辛普森博士将开发改进的贝叶斯建模管道，以处理真实世界数据的时空模型。这项工作跨越了模型规范、后验计算、模型检查、可视化和结果解释等问题。这些进展将在流行的R-INLA和STAN软件包中实施，预计将对一些应用科学研究领域产生影响。

Carefully modelling spatiotemporal data vital in fields as diverse as ecology, epidemiology, and fisheries science. As the size of datasets increases, so does the complexity of the associated statistical models. Dr. Simpson will develop improved Bayesian modelling pipelines to deal with the spatiotemporal models of real-world data. The effort spans issues of model specification, posterior computation, model checking, visualisation, and interpretation of results. The advances will be implemented in the popular R-INLA and Stan software packages, and are expected to have an impact in a number of areas of applied scientific research.