We construct objective priors for regular univariate parametric models designed to produce low coverage probability bias of Bayesian predictive distributions, giving rise to calibrated prediction in repeated use. This is achieved by defining the predictive discrepancy and deriving a class of minimum predictive discrepancy priors. In the multiparameter case these priors will often be data-dependent. The predictive efficacy of alternative global priors can be assessed by comparison with these priors. The effectiveness of the theory is illustrated by a number of examples.

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