Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach

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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Bibliographic Details
Main Author: Joe Suzuki
Format: Article
Language:English
Published: MDPI AG 2015-08-01
Series:Entropy
Subjects:
posterior probability
consistency
minimum description length
universality
discrete and continuous variables
Bayesian network
Online Access:http://www.mdpi.com/1099-4300/17/8/5752

Internet

http://www.mdpi.com/1099-4300/17/8/5752

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