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キーワード:
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要旨:
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opperamp;Winther 05) with covariance decoupling techniques (Wipfamp;Nagarajan 08, Nickischamp;Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.
