Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

In three linesAsymptotic theory for SGLD-Gibbs combining stochastic gradient Langevin dynamics with Gibbs updates for Bayesian inference in latent variable models. Authors derive jump-diffusion limits and propose hyperparameter tuning guidance ensuring statistically meaningful uncertainty quantification.

Summary generated by Claude — human-verified