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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yanbing Zhang Huaiyu Dai |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Electrical and Computer Engineering, NC State University, Raleigh, USA (Yanbing Zhang; Huaiyu Dai) |
| Abstract | In this paper, the asymptotic performance of a recently proposed distributed inference framework, structured variational methods, is investigated. We first distinguish the intra- and inter-cluster inference algorithms as vertex and edge processes respectively. Their difference is illustrated, and convergence rate is derived for the intra-cluster inference procedure which is based on an edge process. Then, viewed as a mixed vertex-edge process, the overall performance of structured variational methods is characterized via the coupling approach. Tradeoff between complexity and performance of this algorithm is also addressed, which provides insights for network design and analysis. |
| Starting Page | 1333 |
| Ending Page | 1337 |
| File Size | 1051101 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424443123 |
| DOI | 10.1109/ISIT.2009.5205928 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Convergence Performance analysis Inference algorithms Algorithm design and analysis Belief propagation Clustering algorithms Wireless networks Distributed algorithms Large-scale systems Intelligent systems |
| Content Type | Text |
| Resource Type | Article |
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