Real-world applications of Bayesian networks
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Learning Bayesian Networks
Reconfigurable computing for learning Bayesian networks
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Parallel Algorithm for Learning Optimal Bayesian Network Structure
The Journal of Machine Learning Research
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Given n random variables and a set of m observations of each of the n variables, the Bayesian network structure learning problem is to learn a directed acyclic graph (DAG) on the n variables such that the implied joint probability distribution best explains the set of observations. Bayesian networks are widely used in many fields including data mining and computational biology. Globally optimal (exact) structure learning of Bayesian networks takes O(n^2@?2^n) time plus the cost of O(n@?2^n) evaluations of an application-specific scoring function whose run-time is at least linear in m. In this paper, we present a parallel algorithm for exact structure learning of a Bayesian network that is communication-efficient and work-optimal up to O(1n@?2^n) processors. We further extend this algorithm to the important restricted case of structure learning with bounded node in-degree and investigate the performance gains achievable because of limiting node in-degree. We demonstrate the applicability of our method by implementation on an IBM Blue Gene/P system and an AMD Opteron InfiniBand cluster and present experimental results that characterize run-time behavior with respect to the number of variables, number of observations, and the bound on in-degree.