New data-driven approaches to improve probabilistic model structure learning - Zhao, Jianjun (2019)
To learn the network structures used in probabilistic models (e.g., Bayesian network), many researchers proposed structure learning algorithms to extract the network structure from data. However, structure learning is a challenging problem due to the extremely large number of possible structure candidates. One challenge relates to structure learning in Bayesian network is the conflicts among local structures obtained from the local structure learning algorithms. This is the so-called symmetry correction problem. Another challenge is the V-structure selection problem, which is related to the determination of edge orientation in Bayesian network.