AI and Big Data

An Introduction to Bayesian Networks

冬日晴好, 下午看完了论文, 对Bayesian Network是什么有了系统的了解.论文是causalnex工具里提到的
Stephenson, Todd Andrew. An introduction to Bayesian network theory and usage. No. REP_WORK. IDIAP, 2000.

该论文主要论述了以下几点:

  • What is Bayesian network
  • Inference Bayesian network: junction tree algorithm
  • Learning Bayesian Network
  • Applications
    • Automatic Speech Recognition: Dynamic Bayesian Network
    • Computer troubleshooting
    • Medical diagnosis
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Structure Learning Algorithm NOTEARS

最近三年扎进了AI领域, 学了很多算法, 最近开始真正拉高维度看AI, AI不仅仅是Machine Learning, 还有State Based, Variable Bases, Logic编程等方法. 最近半年看了The book of Why, 深受启发, 看世界的角度也发生很大变化, 同时也觉得因果推理将是一个值得研究的好领域, 就算目前落地场景不多, 相信未来也是大有可为.

今天静下来, 好好看了在CausalNex库中, 用到的算法NOTEARS, 用于结构学习, 该论文发表在2018的NIPS, 方法神奇, 解决方案简洁, 以下是自己的一些笔记:

Paper: Zheng, Xun, et al. “DAGs with NO TEARS: Continuous optimization for structure learning.” Advances in Neural Information Processing Systems 31 (2018): 9472-9483.

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