置信度传播(英語:belief propagation),又称为乘积和信息传递sum-product message passing),是在贝叶斯网络马尔可夫随机场概率图模型中用于推断的一种信息传递算法。在给定已观测节点时,可以用该算法高效地计算未观测节点的边缘分布。置信度传播在人工智能信息论中十分常见,已成功应用于低密度奇偶检查码Turbo码自由能估计、可满足性英语Satisfiability等不同领域。[1]

置信度传播由美国计算机科学家朱迪亚·珀尔于1982年提出。[2]最初该算法的运用范围仅限于,不久则扩展到多树英语Polytree[3]此后,研究者发现在一般的图中该算法是一种十分有用的近似算法。[4]

参考文献

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  1. ^ Braunstein, A.; Mézard, M.; Zecchina, R. Survey propagation: An algorithm for satisfiability. Random Structures & Algorithms. 2005, 27 (2): 201–226. doi:10.1002/rsa.20057. 
  2. ^ Pearl, Judea. Reverend Bayes on inference engines: A distributed hierarchical approach (PDF). Proceedings of the Second National Conference on Artificial Intelligence. AAAI-82: Pittsburgh, PA. Menlo Park, California: AAAI Press: 133–136. 1982 [2009-03-28]. (原始内容存档 (PDF)于2011-06-04). 
  3. ^ Kim, Jin H.; Pearl, Judea. A computational model for combined causal and diagnostic reasoning in inference systems (PDF). Proceedings of the Eighth International Joint Conference on Artificial Intelligence. IJCAI-83: Karlsruhe, Germany 1: 190–193. 1983 [2016-03-20]. (原始内容存档 (PDF)于2016-04-02). 
  4. ^ Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 2nd. San Francisco, CA: Morgan Kaufmann. 1988. ISBN 1-55860-479-0. 

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