On pruning with the MDL Score.

Eunice Yuh-Jie Chen, Adnan Darwiche, Arthur Choi

Abstract: Highlights • We develop techniques for pruning the search space of Bayesian networks, when enumerating the k-best structures from data. • We identify pruning techniques that apply to score-based structure learning in general, and specific ones for MDL scores. • Empirically, we show our techniques can allow state-of-the-art methods to scale to larger datasets with more variables. 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. Previous article in issue Next article in issue