Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Structure Search and Stability Enhancement of Bayesian Networks
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning Bayesian Networks
Parallel Information-Theory-Based Construction of Genome-Wide Gene Regulatory Networks
IEEE Transactions on Parallel and Distributed Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Bayesian networks (BN) are probabilistic graphical models which are widely utilized in various research areas, including modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and exact solutions are limited to a few tens of variables. In this work, we present a parallel BN structure learning algorithm that combines principles of both heuristic and exact approaches and facilitates learning of larger networks. We demonstrate the applicability of our approach by an implementation on a Cray AMD cluster, and present experimental results for the problem of inferring gene networks. Our approach is work-optimal and achieves nearly perfect scaling.