CORDIS Project
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This project develops new methods for Bayesian inference in high-dimensional models, focusing on uncertainty quantification. It aims to improve the accuracy of posterior distributions and provide guidelines for better practices in statistical analysis across various fields.
I propose novel methods for understanding key aspects that are essentialto the future of Bayesian inference for high- or infinite-dimensionalmodels and data.
By combining my expertise on empirical processes andlikelihood theory with my recent work on posterior contraction I shallforemost lay a mathematical foundation for the Bayesian solution touncertainty quantification in high dimensions.Decades of doubt that Bayesian methods can work for high-dimensionalmodels or data have in the last decade…
UNIVERSITEIT LEIDEN
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