We propose a new method for adjusting samples from an approximate posterior to reduce bias and produce more accurate uncertainty quantification. We do this by optimising a transform of the approximate posterior that maximises a scoring rule. The procedure is Bayesian but has some interesting parallels with frequentist calibration.