A contrastive-divergence-based reward stabilizes reinforcement learning of position-dependent step sizes for gradient-based MCMC, beating constant-step tuning on most of 44 benchmark posteriors.
E.4 Exploring the Sensitivity to G0 The results that we report for posteriordb in the main text set G0 based on 10 4 gold- standard samples from the target
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Harnessing the Power of Reinforcement Learning for Adaptive MCMC
A contrastive-divergence-based reward stabilizes reinforcement learning of position-dependent step sizes for gradient-based MCMC, beating constant-step tuning on most of 44 benchmark posteriors.