概率图模型系列（更新至2017.11.15）
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http://blog.csdn.net/github_36326955
I devote myself to dive into typical algorithms on machine learning and deep learning, especially the application in the area of computational personality. My research interests include computational personality, user portrait, online social network, computational society, and ML/DL. In fact you can find the internal connection between these concepts:
In this blog column, I will introduce some typical algorithms about machine learning and deep learning used in OSNs(Online Social Networks), which means we will include NLP, networks community, information diffusion,and individual recommendation system. Apparently, our ultimate target is to dive into user portrait , especially the issues on your personality analysis.
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目录
概率图模型1：隐马尔科夫(1)
摘要：隐马尔科夫模型；两个基本假设；基本定义；隐马尔科夫模型的3个基本问题；概率计算问题；前向算法；后向算法
概率图模型2：隐马尔科夫(2)
摘要：用Python来实现几个重要概念。
概率图模型3：隐马尔科夫(3)
摘要：实现隐马尔可夫模型的预测算法
概率图模型4：贝叶斯网络
摘要： 为什么要研究贝叶斯网络；朴素贝叶斯模型；基本方法；参数估计；python实现；图与分布；基本任务；基本定义；贝叶斯网的语义；I-Map；因子分解；贝叶斯网；图中的独立性；d-分离；有效迹；可靠性；完备性；弱等价
概率图模型5：无向图入门
摘要：因子与辖域；成对局部和全局马尔科夫性；概率无向图模型；吉布斯分布与无向图因子分解
概率图模型6：条件随机场(1)
摘要：条件随机场的定义；条件随机场的表示；概率计算问题
概率图模型7：推理与流动
摘要：推理模式(causal/evidential/ intercausal reasoning);概率影响的流动，有效迹;
概率图模型8：独立性
摘要：独立性，条件独立性
概率图模型9:d-分离算法
摘要：有效迹，d-分离的基本理论，有向图的BFS(广度优先遍历)，两个小问题，d-分离算法
概率图模型10:I-等价
摘要：I-equivalence/skeleton/sucient condition for I-equivalence/immorality/necessary and sufficient condition for I-equivalence