| EPSRC Reference: |
EP/Y011805/1 |
| Title: |
Robust Foundations for Bayesian Inference |
| Principal Investigator: |
Briol, Dr F |
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| Department: |
Statistical Science |
| Organisation: |
UCL |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 March 2024 |
Ends: |
28 February 2025 |
Value (£): |
59,985
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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This project proposes to study the problem of model misspecification in Bayesian statistics and machine learning. This is a significant practical problem since models are at best a mathematical idealisation of a real-world phenomena. As a result, it is necessary to develop novel statistical and machine learning methods which can perform reasonably well when models are mildly misspecified. But to do so, it is of course crucial to start by defining what is meant by "robustness". Unfortunately, there are no agreed upon definition, as well as no widely applicable approach to measure, or quantify, such robustness. This proposals aims to remedy both of these issues.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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