Graphical models
Graphical models
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DOI：
10.1214/088342304000000026
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发表时间：
2004-02-01
影响因子：
5.7
通讯作者：
Jordan, MI
中科院分区：
文献类型：
--
作者：
Jordan, MI
诸如生物信息学，信息检索，语音处理，图像处理和通信等领域的统计应用通常涉及大规模模型，其中数千或数百万个随机变量以复杂的方式链接。图形模型提供了解决这些问题的一般方法，实际上，这些应用领域的研究人员开发的许多模型都是一般图形模型形式主义的实例。我们回顾了一些基本图形模型的基本思想，包括算法思想，这些算法可以将图形模型部署在大规模的数据分析问题中。我们还介绍了生物信息学，错误控制编码和语言处理中图形模型的示例。
Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in large-scale data analysis problems. We also present examples of graphical models in bioinformatics, error-control coding and language processing.