DSP applications: Signal and image processing with belief propagation

被引：0
|
作者：
Sudderth, Erik B. [1 ]
Freeman, William T. [2 ]
机构：
[1] Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, United States
[2] Massachusetts Institute of Technology, Cambridge, MA, United States
来源：
| 2008年 / Institute of Electrical and Electronics Engineers Inc., 445 Hoes Lane / P.O. Box 1331, Piscataway, NJ 08855-1331, United States卷 / 25期
关键词：
Algorithms - Dynamic programming - Error analysis - Image processing - Statistical methods;
D O I：
10.1109/MSP.2007.914235
中图分类号：
学科分类号：
摘要：
A wide range of efficient algorithms for statistical learning and interference can be applied once a problem has been formulated using a graphical model. This model provides a unifying framework for the design and analysis of error correcting codes. Effective interference algorithm also known as belief propagation (BP) propagates information throughout a graphical model via a series of messages sent between neighboring nodes. The BP was derived as an exact interference algorithm for tree-structured graphical models. It is essentially distributed variant of dynamic programming. However, BP may fail to converge in graphs with cycles, and different message update rules have been proposed allowing faster convergence.
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