Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6858863 | International Journal of Approximate Reasoning | 2018 | 13 Pages |
Abstract
The space of Bayesian network structures is prohibitively large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can be safely ignored by optimal structure learning algorithms. In this paper, we propose new techniques for pruning regions of the search space that can be safely ignored by algorithms that enumerate the k-best Bayesian network structures. Empirically, these techniques allow a state-of-the-art structure enumeration algorithm to scale to datasets with significantly more variables.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Eunice Yuh-Jie Chen, Adnan Darwiche, Arthur Choi,