Uncertainty in Artificial Intelligence
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Bayesian Random Fields: The Bethe-Laplace Approximation
Max Welling, Sridevi Parise
While learning the maximum likelihood value of parameters of an undirected graphical model is hard, modelling the posterior distribution over parameters given data is harder. Yet, undirected models are ubiquitous in computer vision and text modelling (e.g. conditional random fields). But where Bayesian approaches for directed models have been very successful, a proper Bayesian treatment of undirected models in still in its infant stages. We propose a new method for approximating the posterior of the parameters given data based on the Laplace approximation. This approximation requires the computation of the covariance matrix over features which we compute using the linear response approximation based in turn on loopy belief propagation. We develop the theory for conditional and 'unconditional' random fields with or without hidden variables. In the conditional setting we introduce a new variant of bagging suitable for structured domains. Here we run the loopy max-product algorithm on a 'super-graph' composed of graphs for individual models sampled from the posterior and connected by constraints. Experiments on real world data validate the proposed methods.
Pages: 512-519
PS Link:
PDF Link: /papers/06/p512-welling.pdf
AUTHOR = "Max Welling and Sridevi Parise",
TITLE = "Bayesian Random Fields: The Bethe-Laplace Approximation",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "512--519"

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