# Coherence and calibration

I am trying to find good definitions and examples for both these concepts regarding frequentist vs Bayesian statistics. Can anyone please shed light on them and explain them? Furthermore, why are Bayesian methods often considered coherent, while frequentist methods seem to focus on calibration. Finally are default Bayesian methods well calibrated and coherent?

Calibration, unfortunately, has multiple meanings. However, with reference to coherence, a model is well calibrated if it predicts an occurrence $$k$$ with probability $$\alpha$$ and $$k$$ actually happens with long-run frequency $$\alpha$$.