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math.ST2026
Random tree Besov priors: Data-driven regularisation parameter selection
Hanne Kekkonen, Andreas Tataris
We develop a data-driven algorithm for automatically selecting the regularisation parameter in Bayesian inversion under random tree Besov priors. One of the key challenges in Bayes…
math.ST2021
Random tree Besov priors -- Towards fractal imaging
Hanne Kekkonen, Matti Lassas, Eero Saksman +1
We propose alternatives to Bayesian a priori distributions that are frequently used in the study of inverse problems. Our aim is to construct priors that have similar good edge-pre…
math.ST2018
Bernstein-von Mises theorems and uncertainty quantification for linear inverse problems
Matteo Giordano, Hanne Kekkonen
We consider the statistical inverse problem of recovering an unknown function from a linear measurement corrupted by additive Gaussian white noise. We employ a nonparametric Ba…