Journal of Software:2013.24(11):2476-2497

(机器感知与智能教育部重点实验室北京大学, 北京 100871;北京大学 信息科学技术学院 智能科学系, 北京 100871)
Research Progress of Probabilistic Graphical Models:A Survey
(Key Laboratory of Machine Perception Peking University, Ministry of Education, Beijing 100871, China;Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
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Received:July 17, 2013    Revised:August 02, 2013
> 中文摘要: 概率图模型作为一类有力的工具,能够简洁地表示复杂的概率分布,有效地(近似)计算边缘分布和条件分布,方便地学习概率模型中的参数和超参数.因此,它作为一种处理不确定性的形式化方法,被广泛应用于需要进行自动的概率推理的场合,例如计算机视觉、自然语言处理.回顾了有关概率图模型的表示、推理和学习的基本概念和主要结果,并详细介绍了这些方法在两种重要的概率模型中的应用.还回顾了在加速经典近似推理算法方面的新进展.最后讨论了相关方向的研究前景.
中文关键词: 概率图模型  概率推理  机器学习
Abstract:Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
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基金项目:国家自然科学基金(61222307,61075003) 国家自然科学基金(61222307,61075003)
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ZHANG Hong-Yi,WANG Li-Wei,CHEN Yu-Xi.Research Progress of Probabilistic Graphical Models:A Survey.Journal of Software,2013,24(11):2476-2497