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Abstract: One of the major obstacles to using Bayesian methods for pattern recognition has been its computational expense. This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible. This method, "Expectation Propagation," unifies and generalizes two previous techniques: assumeddensity filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. The... (Update)
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BibTeX entry: (Update)
T. P. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, MIT Media Lab, 2001. http://citeseer.ist.psu.edu/minka01family.html More
@misc{ minka01family,
author = "T. Minka",
title = "A family of algorithms for approximate Bayesian inference",
text = "T. P. Minka. A family of algorithms for approximate Bayesian inference.
PhD thesis, MIT Media Lab, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/minka01family.html" }
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