Learning causal Bayesian network structures from experimental data

• 成果类型: Article
• 署名作者: Ellis, Byron; Wong, Wing Hung
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000193
• 发表日期: 2008
• 页码: 778-789
• 关键词: convergence; inference; rates

摘要

We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space Markov chain Monte Carlo, equi-energy sampling, importance weighting, and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures.