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.

翻译：Bayesian 方法具有解决反向问题的有用特性,例如成色图的重建。 先前的分布法引入了正规化,有助于解决错误的问题和减少过分的调整。 实际上,这往往导致低于最佳的后表温度,而Bayesian 方法的全部潜力没有实现。 在本文中,我们利用Bayesian 优化优化了先前分布和后表温度的参数。 高温后表导致更好的预测性能和更好的不确定性校准,我们为微小的CT重建任务展示了这一点。