BriefGPT.xyz
Feb, 2013
学习因果网络的贝叶斯方法
A Bayesian Approach to Learning Causal Networks
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David Heckerman
TL;DR
本文研究了贝叶斯网络建模中的参数独立性、模块化特性、似然等价性等假设。同时介绍了机制独立性和组件独立性这两个新的假设，通过以上全部假设可以将学习无因果网络的方法应用到因果网络中。
Abstract
Whereas acausal
bayesian networks
represent probabilistic independence, causal
bayesian networks
represent
causal relationships
. In this p
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