PhD position on model misspecification sponsored by Microsoft Research

微软研究院赞助的模型错误指定博士职位

• 批准号: 2301648
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2301648
• 关键词: PhD; position; model; misspecification; sponsored

Mechanistic or simulation-based models are used in scientific research to understand complex natural phenomena. A mechanistic model can take the form of ordinary/partial/stochastic differential equations (O/P/SDEs) and can be rigid in form but have the benefit to the scientist of having interpretable and testable parameter settings. In part due to the inflexibility of the model forms, misspecification of the model can lead to computationally expensive inference procedures, and more importantly, misleading conclusions, whereby the parameter estimates are confidently incorrect. There is increasing evidence that the inference framework called approximate Bayesian computation (ABC) is more robust to model misspecification than other inferential approaches. We propose to study the mathematical and statistical properties of this robustness, and explore improvements of current approaches for dealing with model misspecification. The research will be pragmatic, embedding the theory with practical examples (where domain knowledge is understood, and hence misspecification can be detected), including using semi-mechanistic models that are used in the public health domain.

科学研究中使用基于机械或模拟的模型来理解复杂的自然现象。机械模型可以采用常微分方程/偏微分方程/随机微分方程 (O/P/SDE) 的形式，并且形式可以是刚性的，但对科学家来说有好处，因为具有可解释和可测试的参数设置。部分由于模型形式的不灵活性，模型的错误指定可能会导致计算成本高昂的推理过程，更重要的是，导致误导性结论，从而导致参数估计肯定是错误的。越来越多的证据表明，称为近似贝叶斯计算 (ABC) 的推理框架比其他推理方法更能抵抗模型错误指定。我们建议研究这种稳健性的数学和统计特性，并探索改进当前处理模型错误指定的方法。该研究将是务实的，将理论与实际例子结合起来（其中领域知识被理解，因此可以检测到错误指定），包括使用公共卫生领域使用的半机械模型。