Probabilistic Graphical Models 2 Bayesian Network Fundamentals

最新推荐文章于 2025-04-27 13:50:42 发布
原创 最新推荐文章于 2025-04-27 13:50:42 发布 · 479 阅读 · 0 ·
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本文深入探讨了贝叶斯网络在医疗诊断领域的应用，通过理论介绍和实际案例，展示了如何利用贝叶斯网络进行疾病预测和诊断。重点分析了贝叶斯网络的基本原理、独立性概念、条件概率的简化以及在具体场景中的应用，如年龄对驾驶员质量的影响。此外，通过Naive Bayes模型和医学诊断案例，进一步说明了贝叶斯网络在实际问题解决中的优势。

================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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