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This paper develops a scaling limit theory for SGLD-Gibbs to provide principled hyperparameter tuning guidance for meaningful uncertainty quantification in large-scale latent variable models.
This paper proposes new discrete-time approximations for stochastic gradient Langevin dynamics (SGLD) with and without momentum, enabling accurate predictions of stationary covariance, iterate average covariance, and integrated autocorrelation time. The method provides improved tuning guidance for large-sample uncertainty quantification, especially under model misspecification.