无公共边的双圈图上置信传播算法的收敛性和正确性

靳艺香; 杨卫华

doi:10.13568/j.cnki.651094.651316.2022.11.25.0002

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无公共边的双圈图上置信传播算法的收敛性和正确性

数理科学 | 更新时间：2026-01-23

- 无公共边的双圈图上置信传播算法的收敛性和正确性
- 无公共边的双圈图上置信传播算法的收敛性和正确性
- 新疆大学学报（自然科学版中英文） 2023年40卷第3期页码：274-285
- 作者机构：
  
  太原理工大学数学学院
- 作者简介：
- 基金信息：
  
  山西省基础研究计划项目“连通性条件下图的圈结构若干问题研究”（20210302123097）
- DOI：10.13568/j.cnki.651094.651316.2022.11.25.0002
  中图分类号： O157.5
- 纸质出版：2023
- 稿件说明：
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[1]靳艺香,杨卫华.无公共边的双圈图上置信传播算法的收敛性和正确性[J].新疆大学学报(自然科学版)(中英文),2023,40(03):274-285.
[1]靳艺香,杨卫华.无公共边的双圈图上置信传播算法的收敛性和正确性[J].新疆大学学报(自然科学版)(中英文),2023,40(03):274-285. DOI： 10.13568/j.cnki.651094.651316.2022.11.25.0002.

DOI：10.13568/j.cnki.651094.651316.2022.11.25.0002.

摘要

为了研究置信传播算法在无公共边的双圈图上的收敛性

以及其收敛的正确性

提出了无公共边的双圈图的置信传播算法和无公共边的二元双圈图的纠正置信传播算法

并给出了无公共边的双圈图全局收敛的条件.应用这两种算法

对无公共边的双圈图进行仿真实验.结果表明:1）全局收敛率为100%; 2）稳态置信与正确边际分布不同

但配置可能相同;二元稳态纠正置信与正确边际分布完全相同.

Abstract

In order to study the convergence of belief propagation algorithms on double-cycles graphs with no common edge and its convergence correctness

in this paper

a belief propagation algorithm for the double-cycles graphs with no common edge and a correction belief propagation algorithm for the binary double-cycles graphs with no common edge are presented

and a condition of global convergence for the double-cycles graphs with no common edge is given. The two algorithms are used to simulate the double-cycles graphs with no common edge.The results show that: 1) The global convergence percentage is 100%; 2) The steady-state belief is different from the correct marginal distribution

but the configuration may be the same; The binary steady-state correction belief is identical to the correct marginal distribution.

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references

PEARL J. Reverend Bayes on inference engines:a distributed hierarchical approach[C]//Anon. Proceedings of the National Conference on Artificial Intelligence. Pittsburgh:AAAI Press, 1982.

PEARL J. Probabilistic reasoning in intelligent systems:networks of plausible inference[M]. Los Actos, CA:Morgan Kaufmann,1988.

WEISS Y. Correctness of local probability propagation in graphical models with loops[J]. Neural Computation, 2000, 12(1):1-41.

CANTWELL G T, NEWMAN M E J. Message passing on networks with loops[J]. Proceedings of the National Academy of Sciences, 2019, 116(47):23398-23403.

KSCHISCHANG F R, FREY B J. Iterative decoding of compound codes by probability propagation in graphical models[J]. IEEE Journal on Selected Areas in Communications, 1998, 16(2):219-230.

WEISS Y. Belief propagation and revision in networks with loops[M]. Cambridge:Massachusetts Institute of Technology, 1997.

WEISS Y, FREEMAN W T. On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs[J].IEEE Transactions on Information Theory, 2001, 47(2):736-744.

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