Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs
from data is helpful to understand the casual relation between the variable. But Learning BNs is a NP hard problem. This paper
presents a novel hybrid algorithm for learning Markov Equivalence Classes, which combining dependency analysis and search-scoring
approach together. The algorithm uses the constraint to perform a mapping from skeleton to MEC. Experiments show that the
search space was constrained efficiently and the computational performance was improved.
Keywords Bayesian network - Structural learning - Markov equivalence class - condition independence test
Supported by NSFC Major Research Program 60496321, National Natural Science Foundation of China under Grant Nos. 60373098,
60573073, 60603030, 60503016 the National High-Tech Research and Development Plan of China under Grant No. 20060110Z2037,
the Major Program of Science and Technology Development Plan of Jilin Province under Grant No. 20020303, the Science and Technology
Development Plan of Jilin Province under Grant No. 20030523, European Commission under Grant No. TH/Asia Link/010 (111084).