Electronic Journal of Statistics

The explicit form of expectation propagation for a simple statistical model

Andy S. I. Kim and M. P. Wand

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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.

Article information

Electron. J. Statist. Volume 10, Number 1 (2016), 550-581.

Received: December 2014
First available in Project Euclid: 4 March 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62F15: Bayesian inference
Secondary: 62H12: Estimation

Bayesian computing factor graph hierarchical Bayesian models message passing algorithm quadrature variational message passing


Kim, Andy S. I.; Wand, M. P. The explicit form of expectation propagation for a simple statistical model. Electron. J. Statist. 10 (2016), no. 1, 550--581. doi:10.1214/16-EJS1114. https://projecteuclid.org/euclid.ejs/1457123506

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