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* [http://sandeep-aparajit.blogspot.com/2013/06/how-does-conditional-random-field-crf.html Graphical models and Conditional Random Fields]
* [http://sandeep-aparajit.blogspot.com/2013/06/how-does-conditional-random-field-crf.html Graphical models and Conditional Random Fields]
* [https://www.cs.cmu.edu/~epxing/Class/10708/ Probabilistic Graphical Models taught by Eric Xing at CMU]
* [https://www.cs.cmu.edu/~epxing/Class/10708/ Probabilistic Graphical Models taught by Eric Xing at CMU]
==编者推荐==
=== 课程推荐 ===
==== [https://campus.swarma.org/course/1579 概率图模型] ====
人工智能可以看做两个割裂的阶段，一是早期的基于符号的搜索，二是现在的机器学习，概率图模型可以将两者统一起来。概率图模型完美融合了人工智能的各个任务，我们既可以表示知识、推理、概率推断、learning，是为数不多的能够统一全部内容的方法。本课程中，北京师范大学系统科学学院教授张江将从概率理论、贝叶斯网、马尔科夫网和表示学习四部分内容讲解概率图模型。
==== [https://campus.swarma.org/course/1579 概率图模型] ====