Probabilistic Graphical Models 2 Bayesian Network Fundamentals
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================Semantics & Factorization============
1.第一个简单贝叶斯网络
==============Reasoning Patterns==============
============Flow of Probabilistic Influence=============
==============Conditional Independence=============
D,I间无连线，相互独立
=========Independencies in Bayesian Networks==========
以上理论其实都为下图服务，可以看到条件概率被彻底约间。利用（非父亲、非儿子理论）
===============Naive Bayes==============
================Application - Medical Diagnosis=============
================Knowledge Engineering Example==================
good student对应的no accident反而降低了。注意：good student-----》young升高，年轻人毛躁
注意AGE BLOCKgood students 对 Driver_quality的影响
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