A Probabilistic Relaxation Framework for Learning Bayesian Network Structures from Data
Ahmed M. Hassan · 2007
Graphical models have been very promising tools that can effectively model un-certainty, causal relationships, and conditional distributions among random vari-ables. This work proposes a new probabilistic method for learning Bayesian network structures from data. In the proposed method the existence of an edge in the network is no longer considered as a hard or deterministic issue, but rather we assign a certain probability for the existence of each edge. The proposed method uses a global optimization approach, originally developed for cluster-ing and classification problems, to find the set of edges probability that lead to the best network structure. The experimental results show that the proposed ap-proach achieves very promising results compared to other structure learning ap-proaches. Acknowledgments All praises and thanks are due to Allah, The Most Gracious, The Most Merciful, for providing me with the strength, and patience to complete this work.