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arXiv · 1611.04416

On numerical approximation schemes for expectation propagation

Alexis Roche
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Abstract

Several numerical approximation strategies for the expectation-propagation algorithm are studied in the context of large-scale learning: the Laplace method, a faster variant of it, Gaussian quadrature, and a deterministic version of variational sampling (i.e., combining quadrature with variational approximation). Experiments in training linear binary classifiers show that the expectation-propagation algorithm converges best using variational sampling, while it also converges well using Laplace-style methods with smooth factors but tends to be unstable with non-differentiable ones. Gaussian quadrature yields unstable behavior or convergence to a sub-optimal solution in most experiments.

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Alexis Roche. 2016-11-14. On numerical approximation schemes for expectation propagation. https://arxiv.org/abs/1611.04416

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