Prior predictive check

A prior predictive check simulates parameters from the prior distribution and observations from their sampling model, then examines whether those observations are plausible before conditioning on data. This reveals unintended information from apparently broad priors, especially after nonlinear transformations or when scales and correlations are coupled. It differs from a posterior predictive check, which uses the posterior distribution.