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Variational Inference for Latent Variable Models in High Dimensions
Chenyang Zhong, Sumit Mukherjee, Bodhisattva Sen
https://arxiv.org/abs/2506.01893 https…

Variational Inference for Latent Variable Models in High Dimensions
Variational inference (VI) is a popular method for approximating intractable posterior distributions in Bayesian inference and probabilistic machine learning. In this paper, we introduce a general framework for quantifying the statistical accuracy of mean-field variational inference (MFVI) for posterior approximation in Bayesian latent variable models with categorical local latent variables. Utilizing our general framework, we capture the exact asymptotic regime where MFVI `works' for the celeb…