Generalizing Metropolis-Hastings for DAG sampling
Read the original at arxiv.org→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...
Original headline: "Generalizing Metropolis-Hastings for DAG Sampling"
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- Oct 23, 04:00 UTC arXiv cs.LG lead source Generalizing Metropolis-Hastings for DAG Sampling