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Frequentist guarantees of Bayesian Inference with Missing data

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RCLW04 - Early Career Pioneers in Uncertainty Quantification and AI for Science

Missing data is a common roadblock that is needed to be overcome for inference. One natural approach is to use Bayesian methods as a way of imputing the missing data. These methods work well in practise, but a theoretical foundation is missing. One approach to provide these results is to use a Bernstein-von Mises theorem. In this presentation I will discuss our ongoing work and results in proving this result. This is a project with Judith Rousseau.

This talk is part of the Isaac Newton Institute Seminar Series series.

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