The Explicit Form of Expectation Propagation for a Simple Statistical ModelRecent papers (2016)
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.
Publication DateSummer February 2, 2016
Citation InformationAndy Sang Il Kim and Matt Wand. "The Explicit Form of Expectation Propagation for a Simple Statistical Model" Recent papers (2016)
Available at: http://works.bepress.com/matt_wand/14/