SPIN Unprocessed October 23, 2026 ai_technology research
Generalizing Metropolis-Hastings for DAG Sampling
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arXiv:2610.30291v1 Announce Type: new Abstract: A fundamental challenge in Bayesian network structure learning lies in efficiently approximating the posterior distribution of directed acyclic graphs (DAGs). For large, high-dimensional datasets, the task remains beyond the capabilities of current methods. This work advances Markov chain Monte Carlo algorithms by introducing novel moves that simultaneously resample the parents for multiple nodes of the DAG. At the core of these moves is duet sampl
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