Learning Causal Bayesian Network Structures From Experimental Data
从实验数据学习因果贝叶斯网络结构
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.