Calibrating general posterior credible regions
针对模型设定不当导致的后验可信区域覆盖不足问题，提出一个标量调参来控制后验分布分散度，并用蒙特卡洛算法使可信区域达到名义频率覆盖概率。
Summary Calibration of credible regions derived from under- or misspecified models is an important and challenging problem. In this paper, we introduce a scalar tuning parameter that controls the posterior distribution spread, and develop a Monte Carlo algorithm that sets this parameter so that the corresponding credible region achieves the nominal frequentist coverage probability.