Learning Graphical Model Parameters with Approximate Marginal Inference

Justin Domke

doi:10.1109/tpami.2013.31

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arXiv · 1301.3193

Learning Graphical Model Parameters with Approximate Marginal Inference

Justin Domke

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Abstract

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.

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Justin Domke. 2013-01-15. Learning Graphical Model Parameters with Approximate Marginal Inference. https://doi.org/10.1109/tpami.2013.31

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