The explicit form of expectation propagation for a simple statistical model

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Journal Article
Electronic Journal of Statistics, 2016, 10 (1), pp. 550 - 581
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© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of expectation propagation for approximate deterministic Bayesian inference in a simple statistical model. The model corresponds to a random sample from the Normal distribution. The explicit forms, and their derivation, allow a deeper understanding of the issues and challenges involved in practical implementation of expectation propagation for statistical analyses. No auxiliary approximations are used: we follow the expectation propagation prescription exactly. A simulation study shows expectation propagation to be more accurate than mean field variational Bayes for larger sample sizes, but at the cost of considerably more algebraic and computational effort.
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