Efficient inference for hybrid dynamic Bayesian networks

K. C. Chang; H. D. Chen

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Efficient inference for hybrid dynamic Bayesian networks

机译：混合动态贝叶斯网络的高效推理

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摘要

Bayesian networks for static as well as for dynamic cases have been the subject of a great deal of theoretical analysis and practical inference-algorithm development in the research community of artificial intelligence, machine learning, and pattern recognition. After summarizing the well-known theory of discrete and continuous Bayesian networks, we introduce an efficient reasoning scheme into hybrid Bayesian networks. In addition to illustrating the similarities between the dynamic Bayesian networks and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBNs). The proposed method is based on the separation of the dynamic and static nodes, and subsequent hypercubic partitions via the decision tree algorithm. Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the trade-offs of computational complexity and accuracy performance, compared to other exact and approximate methods for applications with uncertainty in a dynamic system.

机译：在人工智能、机器学习和模式识别的研究社区中，静态和动态情况的贝叶斯网络一直是大量理论分析和实践推理算法开发的主题。在总结了著名的离散和连续贝叶斯网络理论之后，我们在混合贝叶斯网络中引入了一种有效的推理方案。除了说明动态贝叶斯网络和卡尔曼滤波之间的相似性外，我们还提出了一种计算效率高的混合动态贝叶斯网络（HDBNs）推理问题的方法。该方法基于动态节点和静态节点的分离，以及随后通过决策树算法进行超立方分区。实验表明，在动态系统中具有不确定性的应用中，HDBN中使用的新算法在计算复杂性和精度性能的权衡方面具有较高的统计置信度。

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来源
《Optical Engineering》 |2005年第7期|077201-1-077201-7-0|共7页
作者
K. C. Chang; H. D. Chen;
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作者单位

George Mason University Department of Systems Engineering and Operations Research MS 4A6, 4400 University Drive Fairfax, Virginia 22030;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
关键词
dynamic Bayesian networks; hybrid Bayesian networks; decision tree; estimation for dynamic systems;

机译：动态贝叶斯网络;混合贝叶斯网络;决策树;动态系统的估计;

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Efficient inference for hybrid dynamic Bayesian networks

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