Finding the K-Best Equivalence Classes of Bayesian Network Structures for Model Averaging
Abstract
In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs (Tian, He and Ram 2010). We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space to achieve the same quality of approximation. Our algorithm goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery.
Cite
Text
Chen and Tian. "Finding the K-Best Equivalence Classes of Bayesian Network Structures for Model Averaging." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9064Markdown
[Chen and Tian. "Finding the K-Best Equivalence Classes of Bayesian Network Structures for Model Averaging." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/chen2014aaai-finding/) doi:10.1609/AAAI.V28I1.9064BibTeX
@inproceedings{chen2014aaai-finding,
title = {{Finding the K-Best Equivalence Classes of Bayesian Network Structures for Model Averaging}},
author = {Chen, Yetian and Tian, Jin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {2431-2438},
doi = {10.1609/AAAI.V28I1.9064},
url = {https://mlanthology.org/aaai/2014/chen2014aaai-finding/}
}