arXiv stat.ML
· Papers
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
arXiv:2606.16073v2 Announce Type: replace-cross Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling. While Markov chain Monte Carlo (MCMC) methods provide asymptotic guarantees, they often suffer from slow mixing and high comput