  Likelihood based-learning of graphical models faces challenges of
computational-complexity and robustness to model mis-specification. This paper
studies methods that fit parameters directly to maximize a measure of the
accuracy of predicted marginals, taking into account both model and inference
approximations at training time. Experiments on imaging problems suggest
marginalization-based learning performs better than likelihood-based
approximations on difficult problems where the model being fit is approximate
in nature.
