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Conference Paper

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

MPS-Authors

Seeger,  M.
Max Planck Society;

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Nickisch,  H.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Seeger, M., & Nickisch, H. (2011). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. In 14th International Conference on Artificial Intelligence and Statistics (pp. 652-660).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4D2A-1
Abstract
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 (Opper&Winther, 2005) with covariance decoupling techniques (Wipf&Nagarajan, 2008; Nickisch&Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.