Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Artificial Intelligence
2015-01-20 v1 Machine Learning
Machine Learning
Abstract
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.
Cite
@article{arxiv.1501.04370,
title = {Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling},
author = {Ru He and Jin Tian and Huaiqing Wu},
journal= {arXiv preprint arXiv:1501.04370},
year = {2015}
}
Comments
51 pages