A Bayesian Approach to Learning Causal Networks
A Bayesian Approach to Learning Causal Networks
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DOI：
10.1017/cbo9780511611308.012
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发表时间：
2007-01-01
期刊：
影响因子：
--
通讯作者：
Heckerman, David
中科院分区：
文献类型：
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作者：
Heckerman, David
贝叶斯方法已被开发用于从数据中学习贝叶斯网络。这项工作的大部分集中在贝叶斯网络上，贝叶斯网络被解释为概率条件独立性的表示，而不考虑因果关系。其他研究人员表明，因果解释非常重要，因为它使我们能够预测某个领域干预措施的影响。在本章中，我们将学习非因果贝叶斯网络的贝叶斯方法扩展到因果贝叶斯网络。
Bayesian methods have been developed for learning Bayesian networks from data. Most of this work has concentrated on Bayesian networks interpreted as a representation of probabilistic conditional independence without considering causation. Other researchers have shown that having a causal interpretation can be important because it allows us to predict the effects of interventions in a domain. In this chapter, we extend Bayesian methods for learning acausal Bayesian networks to causal Bayesian networks.