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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/2552

Title: Density evolution for expectation propagation
Authors: Walsh, John MacLaren
Keywords: Expectation Propagation;Bayes Procedures;Distributed Iterative Decoding And Estimation;Belief Propagation
Issue Date: 15-Apr-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Paper presented at 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI.
Abstract: Expectation propagation (EP) [1, 2, 3, 4] is a theoretical extension of the belief propagation family of message passing algorithms [5, 6] for statistical inference which allows for efficient handling of models with continuous random variables as well as second or higher order correlation via the use of standard exponential families of probability measures [7, 8, 9]. Here we provide theoretically rigorous justifications for the use of density evolution [10, 11] to analyze the convergence and performance behavior of the family of algorithms in the large system regime by extending and expanding on the corresponding results for belief propagation decoding and turbo decoding.
URI: http://dx.doi.org/10.1109/ICASSP.2007.366293
Appears in Collections:Faculty Research and Publications (ECE)

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