Hugging Face Trending Papers
Generalizing Metropolis-Hastings for DAG Sampling
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arXiv Machine Learning
Oct 23
Generalizing Metropolis-Hastings for DAG Sampling
The paper introduces duet sampling, a generalization of the Metropolis-Hastings algorithm that selects two proposal distributions from a pool at each iteration to resample parents for multiple nodes in a directed acyclic graph (DAG). This approach enables Markov chain moves that mix more efficiently without extra computational cost, outperforming existing state‑of‑the‑art methods in experiments. The authors demonstrate that their novel moves, both alone and combined, achieve superior performance even when previous methods also adopt duet sampling.
arXiv Machine Learning
Aug 31
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
arXiv Machine Learning
Sep 15
The Marked Edge Walk: A Novel MCMC Algorithm for Sampling of Graph Partitions
arXiv:2510.17714v3 Announce Type: replace-cross Abstract: Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans modeled as a graph partit...
arXiv Statistics ML
Aug 25
Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
arXiv:2509.13267v3 Announce Type: replace-cross Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
arXiv Machine Learning
Jun 16
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
arXiv:2606. 16073v1 Announce Type: new Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling.
arXiv Machine Learning
Jun 18
Structured Inference with Large Language Gibbs
arXiv:2606. 19264v1 Announce Type: new Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem.