Bayesian Model Scoring in Markov Random Fields

@inproceedings{Parise2006BayesianMS,
  title={Bayesian Model Scoring in Markov Random Fields},
  author={Sridevi Parise and Max Welling},
  booktitle={NIPS},
  year={2006}
}
Scoring structures of undirected graphical models by means of evaluating the marginal likelihood is very hard. The main reason is the presence of the partition function which is intractable to evaluate, let alone integrate over. We propose to approximate the marginal likelihood by employing two levels of approximation: we assume normality of the posterior (the Laplace approximation) and approximate all remaining intractable quantities using belief propagation and the linear response… CONTINUE READING

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