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中文摘要：
近年来概率图模型已成为不确定性推理的研究热点,在人工智能、机器学习与计算机视觉等领域有广阔的应用前景。根据网络结构与查询问题类型的不同,系统地综述了概率图模型的推理算法。首先讨论了贝叶斯网络与马尔可夫网络中解决概率查询问题的精确推理算法与近似推理算法,其中主要介绍精确推理中的VE算法、递归约束算法和团树算法,以及近似推理中的变分近似推理和抽样近似推理算法,并给出了解决MAP查询问题的常用推理算法;然后分别针对混合网络的连续与混合情况阐述其推理算法,并分析了暂态网络的精确推理、近似推理以及混合情况下的推理;最后指出了概率图模型推理方法未来的研究方向。
英文摘要：
In recent years,probabilistic graphical models have become the focus of the research in uncertainty inference,because of their bright prospect for the application in artificial intelligence,machine learning,computer vision and so forth.According to different network structures and query questions,the inference algorithms of probabilistic graphical models were summarized in a systematic way.First,exact and approximate inference algorithms for solving the probability queries in Bayesian network and Markov network were discussed,including variable elimination algorithms,conditioning algorithms,clique tree algorithms,variational inference algorithms and sampling algorithms.The common algorithms for solving MAP queries were also introduced.Then the inference algorithms in hybrid networks were described respectively for continuous or hybrid cases.In addition,this work analyzed the exact and approximate inference in temporal networks,and described inference in continuous or hybrid cases for temporal networks.Finally,this work raised some questions that the inference algorithms of probabilistic graphical models are facing with and discussed their development in the future.
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