Derives joint asymptotic jump-diffusion limit for global parameters and latent variables in SGLD-Gibbs under space-time rescaling, yielding explicit hyperparameter tuning guidance for calibrated uncertainty quantification.
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Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
Derives joint asymptotic jump-diffusion limit for global parameters and latent variables in SGLD-Gibbs under space-time rescaling, yielding explicit hyperparameter tuning guidance for calibrated uncertainty quantification.