Learning Graphical Model Parameters with Approximate Marginal Inference
Learning Graphical Model Parameters with Approximate Marginal Inference
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 …