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

Entropy

Joe Suzuki¹ 

[1] Department of Mathematics, Graduate School of Science, Osaka University, Toyonaka-shi 560-0043, Japan; E-Mail

关键词: posterior probability;    consistency;    minimum description length;    universality;    discrete and continuous variables;    Bayesian network;   

DOI  :  10.3390/e17085752

来源: mdpi

【 摘 要 】

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 inis arbitrary, which implies that the Hannan–Quinn proposition holds.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.