Bayesian Model Averaging Using the k-best Bayesian Network Structures
Machine Learning
2012-03-19 v1 Artificial Intelligence
Machine Learning
Abstract
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that the method outperforms the model selection method and the state of-the-art MCMC methods.
Cite
@article{arxiv.1203.3520,
title = {Bayesian Model Averaging Using the k-best Bayesian Network Structures},
author = {Jin Tian and Ru He and Lavanya Ram},
journal= {arXiv preprint arXiv:1203.3520},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)