Learning hybrid Bayesian networks from data
Learning in graphical models
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Learning Bayesian Networks
Bioinformatics
Finding a path is harder than finding a tree
Journal of Artificial Intelligence Research
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning bayesian networks does not have to be NP-Hard
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
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We propose an algorithm for learning an optimal Bayesian network from data. Our method is addressed to biological applications, where usually datasets are small, and sets of random variables are large. Moreover, we assume that there is no need to examine the acyclicity of the graph. We provide polynomial bounds (with respect to the number of random variables) for time complexity of our algorithm for two generally used scoring criteria: Minimal Description Length and Bayesian-Dirichlet equivalence. Then we show how to adapt these criteria to work with continuous data and prove polynomial bounds for adapted scores. Finally, we briefly describe applications of proposed algorithm in computational biology.