A stochastic mirror Langevin dynamics sampler with subsampling and Wasserstein-based post-processing yields accurate posterior samples and variance estimates for large-scale Bayesian GLMMs.
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Safe, Scalable, and Accurate Bayes Posterior Sampling for Large-Data Generalized Linear Mixed Models
A stochastic mirror Langevin dynamics sampler with subsampling and Wasserstein-based post-processing yields accurate posterior samples and variance estimates for large-scale Bayesian GLMMs.