# Bayesian neural network priors for edge-preserving inversion

@article{Li2021BayesianNN, title={Bayesian neural network priors for edge-preserving inversion}, author={Chen Li and Matthew M. Dunlop and Georg Stadler}, journal={ArXiv}, year={2021}, volume={abs/2112.10663} }

We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori. A class of prior distributions based on the output of neural networks with heavy-tailed weights is introduced, motivated by existing results concerning the infinitewidth limit of such networks. We show theoretically that samples from such priors have desirable discontinuous-like properties even when the network width is finite, making them appropriate for edge…

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