Scaling Up Bayesian DAG Sampling
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Abstract (arXiv)
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.
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References · 15
- 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
- 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
- 2021A survey of Bayesian Network structure learningcited by 87
- 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
- 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
- 2013Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 42
- 2014Addendum on the scoring of Gaussian directed acyclic graphical modelscited by 33
- 2015Partition MCMC for inference on acyclic digraphscited by 33
- 2020Towards Scalable Bayesian Learning of Causal DAGscited by 33
- 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
- 2013Theory Refinement on Bayesian Networkscited by 28
- 2018Efficient Sampling and Structure Learning of Bayesian Networkscited by 28
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