Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
The Journal of Machine Learning Research
An efficient and scalable algorithm for local Bayesian network structure discovery
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Permutation testing improves Bayesian network learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
An experimental comparison of hybrid algorithms for bayesian network structure learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion
The Journal of Machine Learning Research
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Modern Bayesian Network learning algorithms are time-efficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development, variable selection, and causal discovery. The quality of the models, however, has often only been empirically evaluated; the available theoretical results typically guarantee asymptotic correctness (consistency) of the algorithms. This paper describes theoretical bounds on the quality of a fundamental Bayesian Network local-learning task in the finite sample using theories for controlling the False Discovery Rate. The behavior of the derived bounds is investigated across various problem and algorithm parameters. Empirical results support the theory which has immediate ramifications in the design of new algorithms for Bayesian Network learning, variable selection and causal discovery.