Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach
We consider the problem of learning a Bayesian network structure given n examples and the prior probability based on maximizing the posterior probability. We propose an algorithm that runs in O(n log n) time and that addresses continuous variables and discrete variables without assuming any class of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-08-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/8/5752 |