Model adequacy: A bayesian viewpoint

We present a BAYEsian theory for testing model adequacy which provides exact results whether the model is linear or nonlinear in the parameters. We consider two cases: (a) replicated data exists, and (b) no replicated data exists but an external estimate of the variance is available. Furthermore, we derive a useful approximation for testing model adequacy when the model is nonlinear in the parameters.