Generalizing Metropolis-Hastings for DAG Sampling
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 me…
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- 2026-10-23 04:00 · arXiv cs.LG
Generalizing Metropolis-Hastings for DAG Sampling