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Natalia Bochkina is a Reader at the School of Mathematics, University of Edinburgh, specializing in Bayesian nonparametric wavelet regression and Bayesian hierarchical modelling of genomic data. Her research primarily focuses on the optimality of priori Besov regularity properties of estimators and the challenges posed by small sample sizes in genomic applications, such as gene expression microarrays and NMR metabolic spectra. Bochkina is also actively involved in studying inverse problems and the sensitivity of prior choices in Bayesian frameworks. Her expertise combines mathematical rigor with practical applications in statistical genomics, making significant contributions to contemporary research in her field.
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