Bayes posterior just quick and dirty confidence ?

Bayes (1763) introduced the observed likelihood function to statistical inference and provided a weight function to calibrate the parameter; he also introduced a confidence distribution on the parameter space but restricted attention to models now called location models; of course the names likelihood and confidence did not appear until much later: Fisher (1922) for likelihood and Neyman (1937) for confidence. Lindley (1958) showed that the Bayes and the confidence results were different when the model was not location. This paper examines the occurrence of true statements from the Bayes approach and from the confidence approach, and shows that the proportion of true statements in the Bayes case depends critically on the presence of linearity in the model; and with departure from this linearity the Bayes approach can be seriously misleading. Bayesian integration of weighted likelihood provides a first order linear approximation to confidence, but without linearity can give substantially incorrect results.