Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty
English summary
This tutorial article offers an intuitive introduction to probabilistic graphical models for reasoning under uncertainty. It covers directed Bayesian networks, which represent causal dependencies, and undirected Markov networks, which capture symmetric associations. The guide also discusses weighted logical rules, illustrating how to combine logical knowledge with probabilistic weights. The material is presented as an accessible resource for data science practitioners to understand core concepts in structured uncertainty.
Chinese summary
本教程文章直观介绍了用于不确定性推理的概率图模型。内容涵盖表示因果依赖关系的有向贝叶斯网络，以及捕捉对称关联的无向马尔可夫网络。指南还讨论了加权逻辑规则，展示了如何将逻辑知识与概率权重相结合。材料以易懂的方式呈现，为数据科学从业者理解结构化不确定性核心概念提供资源。
Key points
Introduces Bayesian networks as directed graphical models for causal reasoning.
介绍贝叶斯网络作为用于因果推理的有向图模型。
Explains Markov networks as undirected models capturing symmetric dependencies.
解释马尔可夫网络作为捕捉对称依赖关系的无向模型。
Covers weighted logical rules to integrate logic and probability.
涵盖结合逻辑与概率的加权逻辑规则。
Presented as an accessible tutorial for practitioners new to probabilistic graphical models.
以适合初学者的教程形式呈现，面向概率图模型新手从业者。