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.

在科学研究中使用机械模型或基于模拟的模型来理解复杂的自然现象。力学模型可以采用普通/偏/随机微分方程(O/P/SDE)的形式，形式上可以是刚性的，但有利于科学家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导致计算昂贵的推理过程，更重要的是，导致误导性的结论，从而参数估计肯定是不正确的。越来越多的证据表明，与其他推理方法相比，被称为近似贝叶斯计算(ABC)的推理框架对模型误指定具有更强的鲁棒性。我们建议研究这种稳健性的数学和统计特性，并探索当前处理模型误指定的方法的改进。这项研究将是务实的，将理论嵌入到实际例子中(在这些例子中，领域知识得到理解，因此可以检测到错误的说明)，包括使用公共卫生领域使用的半机械模型。