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Statistical inference for misspecified mechanistic models


Project Description

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 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.

This project will be jointly supervised by Professor Richard Wilkinson (University of Sheffield) and Dr Ted Meeds (Microsoft Research (MSR) Cambridge). The student will be expected to spend some time visiting MSR Cambridge.

Science Graduate School:
As a PhD student in one of the science departments at the University of Sheffield, you’ll be part of the Science Graduate School – a community of postgraduate researchers working across biology, chemistry, physics, mathematics and psychology. You’ll get access to training opportunities designed to support your career development by helping you gain professional skills that are essential in all areas of science. You’ll be able to learn how to recognise good research and research behaviour, improve your communication abilities and experience technologies that are used in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more.

Funding Notes

This studentship is supported by Microsoft Research and EPSRC through Microsoft’s PhD Scholarship Programme. It will provide up to 4 years of funding with an enhanced stipend of £18,777 per annum, and comes with a generous training budget for conference travel to UK and international meetings, as well as for time spent visiting Microsoft Research in Cambridge.

Eligibility: This studentship is available to UK and EU students who meet the UK residency requirements. Applicants should have a good first degree in maths, computer science, or physics.

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