Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribution. We address the question of how to adjust these approximate posteriors so that they appropriately capture uncertainty. vWe introduce two methods that extend simulation-based calibration checking (SBC) to widen approximate posterior uncertainty intervals to aim for marginal calibration. We demonstrate these methods in several experimental settings, and we discuss the challenge of calibration using posterior inferences and the potential for posterior recalibration of hierarchical models.

翻译：贝叶斯推断通常通过近似方法实现，这可能导致区间估计过窄，无法完全捕捉后验分布中的不确定性。我们探讨如何调整这些近似后验，使其能够适当地捕获不确定性。我们提出了两种方法，将基于模拟的校准检查（SBC）扩展为放宽近似后验的不确定性区间，以实现边际校准。我们在多个实验设置中展示了这些方法，并讨论了使用后验推断进行校准的挑战，以及层次模型后验重校准的潜力。