  Bayesian methods feature useful properties for solving inverse problems, such
as tomographic reconstruction. The prior distribution introduces
regularization, which helps solving the ill-posed problem and reduces
overfitting. In practice, this often results in a suboptimal posterior
temperature and the full potential of the Bayesian approach is not realized. In
this paper, we optimize both the parameters of the prior distribution and the
posterior temperature using Bayesian optimization. Well-tempered posteriors
lead to better predictive performance and improved uncertainty calibration,
which we demonstrate for the task of sparse-view CT reconstruction.
