MAP is the optimization of prior times likelihood in bayesion method.
MAP is not invariant to paramerterization. It is a point estimation and tend to squeeze the value around the point, flat all other point.
A more complete model is just can model wide ranges of possible data set, spread the probability distribution, but the maginal likelihood (integrating parameter(integral p(D|theta)p(theta))) is not necessarily increasing.
