Scaling Up Bayesian DAG Sampling
This work addresses efficiency bottlenecks in Bayesian network structure inference, which is incremental but important for practitioners in fields like machine learning and statistics.
The paper tackled the problem of improving the efficiency of Bayesian DAG sampling by introducing two techniques: an efficient implementation of basic moves and a preprocessing method to prune parent sets. The result was substantial efficiency gains compared to previous methods, as shown in an empirical study.
Bayesian inference of Bayesian network structures is often performed by sampling directed acyclic graphs along an appropriately constructed Markov chain. We present two techniques to improve sampling. First, we give an efficient implementation of basic moves, which add, delete, or reverse a single arc. Second, we expedite summing over parent sets, an expensive task required for more sophisticated moves: we devise a preprocessing method to prune possible parent sets so as to approximately preserve the sums. Our empirical study shows that our techniques can yield substantial efficiency gains compared to previous methods.