• Corpus ID: 245117718

Laplace priors and spatial inhomogeneity in Bayesian inverse problems

@inproceedings{Agapiou2021LaplacePA,
  title={Laplace priors and spatial inhomogeneity in Bayesian inverse problems},
  author={Sergios Agapiou and Sven Wang},
  year={2021}
}
Spatially inhomogeneous functions, which may be smooth in some regions and rough in other regions, are modelled naturally in a Bayesian manner using so-called Besov priors which are given by random wavelet expansions with Laplace-distributed coefficients. This paper studies theoretical guarantees for such prior measures – specifically, we examine their frequentist posterior contraction rates in the setting of non-linear inverse problems with Gaussian white noise. Our results are first derived… 
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