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Density evolution for expectation propagation
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2552
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| 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
http://hdl.handle.net/1860/2552 |
| Appears in Collections: | Faculty Research and Publications (ECE)
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