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Natalia Bochkina is a Reader in the School of Mathematics. Her research primarily focuses on Bayesian nonparametric wavelet regression, emphasizing optimality and prior Besov regularity properties of estimators. She applies Bayesian hierarchical modeling techniques to genomics data, particularly in gene expression microarrays and NMR metabolic spectra with characterized variables and a small number of observations. Furthermore, she is involved in research concerning inverse problems, particularly in relation to the sensitivity and choice of priors.
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