fbpx
维基百科

置信度传播

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 编辑

  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 (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 (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. 

延伸阅读 编辑

  • Bickson, Danny. (2009). Gaussian Belief Propagation Resource Page (页面存档备份,存于互联网档案馆) —Webpage containing recent publications as well as Matlab source code.
  • Bishop, Christopher M. Chapter 8: Graphical models (PDF). Pattern Recognition and Machine Learning. Springer. 2006: 359–418 [2014-03-20]. ISBN 0-387-31073-8. (原始内容 (PDF)于2016-03-20). 
  • Coughlan, James. (2009). .
  • Koch, Volker M. (2007). —A tutorial-style dissertation
  • Löliger, Hans-Andrea. An Introduction to Factor Graphs. IEEE Signal Proc. Mag. 2004, 21: 28–41 [2017-10-19]. (原始内容于2017-05-17). 
  • Mackenzie, Dana (2005). "Communication Speed Nears Terminal Velocity (页面存档备份,存于互联网档案馆)", New Scientist. 9 July 2005. Issue 2507 (Registration required)
  • Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press. 2007 [2017-10-19]. ISBN 0-521-87315-0. (原始内容于2016-03-03). 
  • Yedidia, J.S.; Freeman, W.T.; Weiss, Y. Understanding Belief Propagation and Its Generalizations. Lakemeyer, Gerhard; Nebel, Bernhard (编). Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann. January 2003: 239–236 [2009-03-30]. ISBN 1-55860-811-7. (原始内容于2020-12-05). 
  • Yedidia, J.S.; Freeman, W.T.; Weiss, Y. Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory. July 2005, 51 (7): 2282–2312 [2009-03-28]. doi:10.1109/TIT.2005.850085. (原始内容于2009-04-18). 

置信度传播, 英語, belief, propagation, 又称为乘积和信息传递, product, message, passing, 是在贝叶斯网络, 马尔可夫随机场等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, 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, 由美国计算机科学家朱迪亚, 珀尔于1982年提出, 最初该算法的运用范围. 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 马尔可夫随机场等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树 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 编辑 Braunstein A Mezard M Zecchina R Survey propagation An algorithm for satisfiability Random Structures amp Algorithms 2005 27 2 201 226 doi 10 1002 rsa 20057 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 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 Pearl Judea Probabilistic Reasoning in Intelligent Systems Networks of Plausible Inference 2nd San Francisco CA Morgan Kaufmann 1988 ISBN 1 55860 479 0 延伸阅读 编辑Bickson Danny 2009 Gaussian Belief Propagation Resource Page 页面存档备份 存于互联网档案馆 Webpage containing recent publications as well as Matlab source code Bishop Christopher M Chapter 8 Graphical models PDF Pattern Recognition and Machine Learning Springer 2006 359 418 2014 03 20 ISBN 0 387 31073 8 原始内容存档 PDF 于2016 03 20 Coughlan James 2009 A Tutorial Introduction to Belief Propagation Koch Volker M 2007 A Factor Graph Approach to Model Based Signal Separation A tutorial style dissertation Loliger Hans Andrea An Introduction to Factor Graphs IEEE Signal Proc Mag 2004 21 28 41 2017 10 19 原始内容存档于2017 05 17 Mackenzie Dana 2005 Communication Speed Nears Terminal Velocity 页面存档备份 存于互联网档案馆 New Scientist 9 July 2005 Issue 2507 Registration required Wymeersch Henk Iterative Receiver Design Cambridge University Press 2007 2017 10 19 ISBN 0 521 87315 0 原始内容存档于2016 03 03 Yedidia J S Freeman W T Weiss Y Understanding Belief Propagation and Its Generalizations Lakemeyer Gerhard Nebel Bernhard 编 Exploring Artificial Intelligence in the New Millennium Morgan Kaufmann January 2003 239 236 2009 03 30 ISBN 1 55860 811 7 原始内容存档于2020 12 05 Yedidia J S Freeman W T Weiss Y Constructing free energy approximations and generalized belief propagation algorithms IEEE Transactions on Information Theory July 2005 51 7 2282 2312 2009 03 28 doi 10 1109 TIT 2005 850085 原始内容存档于2009 04 18 取自 https zh wikipedia org w index php title 置信度传播 amp oldid 70425742, 维基百科,wiki,书籍,书籍,图书馆,

文章

,阅读,下载,免费,免费下载,mp3,视频,mp4,3gp, jpg,jpeg,gif,png,图片,音乐,歌曲,电影,书籍,游戏,游戏。