Scaling Up Bayesian DAG Sampling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908618937335808 |
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| author | Nikzad, Daniele Zhilkin, Alexander Harviainen, Juha Kuipers, Jack Moffa, Giusi Koivisto, Mikko |
| author_facet | Nikzad, Daniele Zhilkin, Alexander Harviainen, Juha Kuipers, Jack Moffa, Giusi Koivisto, Mikko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Up Bayesian DAG Sampling Nikzad, Daniele Zhilkin, Alexander Harviainen, Juha Kuipers, Jack Moffa, Giusi Koivisto, Mikko Machine Learning Artificial Intelligence 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. |
| title | Scaling Up Bayesian DAG Sampling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25254 |